{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入需要的库\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.utils import resample\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n",
    "from sklearn.feature_selection import SelectKBest, mutual_info_classif\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据\n",
    "data = pd.read_csv(\"C:/Users/86166/Desktop/大三上/python数据分析与应用/实验三/HR_Analytics.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除重复值\n",
    "data.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除缺失值\n",
    "data.dropna(subset=['YearsWithCurrManager'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除 'StandardHours' 字段\n",
    "data.drop('StandardHours', axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 转换字段为'category'数据类型\n",
    "category_columns = ['AgeGroup', 'Attrition', 'BusinessTravel', 'Department',\n",
    "                    'EducationField', 'Gender', 'JobRole', 'MaritalStatus', 'SalarySlab']\n",
    "\n",
    "data[category_columns] = data[category_columns].astype('category')\n",
    "# 转换EmpID为字符串类型\n",
    "data['EmpID'] = data['EmpID'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['BusinessTravel'] = data['BusinessTravel'].replace('TravelRarely', 'Travel_Rarely')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Age     BusinessTravel              Department  Education  \\\n",
      "0      18      Travel_Rarely  Research & Development          3   \n",
      "1      18      Travel_Rarely                   Sales          3   \n",
      "2      18  Travel_Frequently                   Sales          3   \n",
      "3      18         Non-Travel  Research & Development          2   \n",
      "4      18         Non-Travel  Research & Development          1   \n",
      "...   ...                ...                     ...        ...   \n",
      "1475   60      Travel_Rarely  Research & Development          3   \n",
      "1476   60  Travel_Frequently                   Sales          3   \n",
      "1477   60      Travel_Rarely                   Sales          4   \n",
      "1478   60      Travel_Rarely                   Sales          4   \n",
      "1479   60      Travel_Rarely  Research & Development          4   \n",
      "\n",
      "     EducationField  EnvironmentSatisfaction  Gender  JobInvolvement  \\\n",
      "0     Life Sciences                        3    Male               3   \n",
      "1           Medical                        4  Female               2   \n",
      "2         Marketing                        2    Male               3   \n",
      "3     Life Sciences                        2    Male               3   \n",
      "4           Medical                        3    Male               3   \n",
      "...             ...                      ...     ...             ...   \n",
      "1475  Life Sciences                        1  Female               3   \n",
      "1476      Marketing                        3  Female               2   \n",
      "1477      Marketing                        1    Male               3   \n",
      "1478      Marketing                        2    Male               4   \n",
      "1479        Medical                        3    Male               1   \n",
      "\n",
      "      JobLevel  JobSatisfaction MaritalStatus  MonthlyIncome  \\\n",
      "0            1                3        Single           1420   \n",
      "1            1                3        Single           1200   \n",
      "2            1                2        Single           1878   \n",
      "3            1                4        Single           1051   \n",
      "4            1                3        Single           1904   \n",
      "...        ...              ...           ...            ...   \n",
      "1475         5                1       Married          19566   \n",
      "1476         3                1       Married          10266   \n",
      "1477         2                1        Single           5405   \n",
      "1478         2                4      Divorced           5220   \n",
      "1479         3                4      Divorced          10883   \n",
      "\n",
      "      RelationshipSatisfaction  WorkLifeBalance  YearsAtCompany  \\\n",
      "0                            3                3               0   \n",
      "1                            1                3               0   \n",
      "2                            4                3               0   \n",
      "3                            4                3               0   \n",
      "4                            4                3               0   \n",
      "...                        ...              ...             ...   \n",
      "1475                         4                1              29   \n",
      "1476                         4                4              18   \n",
      "1477                         4                3               2   \n",
      "1478                         2                3              11   \n",
      "1479                         3                4               1   \n",
      "\n",
      "      YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \\\n",
      "0                      0                        0                   0.0   \n",
      "1                      0                        0                   0.0   \n",
      "2                      0                        0                   0.0   \n",
      "3                      0                        0                   0.0   \n",
      "4                      0                        0                   0.0   \n",
      "...                  ...                      ...                   ...   \n",
      "1475                   8                       11                  10.0   \n",
      "1476                  13                       13                  11.0   \n",
      "1477                   2                        2                   2.0   \n",
      "1478                   7                        1                   9.0   \n",
      "1479                   0                        0                   0.0   \n",
      "\n",
      "     Attrition  \n",
      "0          Yes  \n",
      "1           No  \n",
      "2          Yes  \n",
      "3           No  \n",
      "4          Yes  \n",
      "...        ...  \n",
      "1475        No  \n",
      "1476        No  \n",
      "1477        No  \n",
      "1478        No  \n",
      "1479        No  \n",
      "\n",
      "[1416 rows x 19 columns]\n"
     ]
    }
   ],
   "source": [
    "# 选择特征和目标变量\n",
    "features = ['Age', 'BusinessTravel', 'Department', 'Education', 'EducationField',\n",
    "            'EnvironmentSatisfaction', 'Gender', 'JobInvolvement', 'JobLevel',\n",
    "            'JobSatisfaction', 'MaritalStatus', 'MonthlyIncome', 'RelationshipSatisfaction',\n",
    "            'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole',\n",
    "            'YearsSinceLastPromotion', 'YearsWithCurrManager', 'Attrition']\n",
    "\n",
    "new_data = data[features]\n",
    "print(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\86166\\AppData\\Local\\Temp\\ipykernel_4852\\852593787.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_data['BusinessTravel'] = new_data['BusinessTravel'].map({\n",
      "C:\\Users\\86166\\AppData\\Local\\Temp\\ipykernel_4852\\852593787.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_data['Gender'] = new_data['Gender'].map({\n",
      "C:\\Users\\86166\\AppData\\Local\\Temp\\ipykernel_4852\\852593787.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_data['MaritalStatus'] = new_data['MaritalStatus'].map({\n",
      "C:\\Users\\86166\\AppData\\Local\\Temp\\ipykernel_4852\\852593787.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_data['Attrition'] = new_data['Attrition'].map({\n"
     ]
    }
   ],
   "source": [
    "# 将'BusinessTravel'列中的值进行映射\n",
    "# 'Non-Travel'映射为0，'Travel_Rarely'映射为1，'Travel_Frequently'映射为2\n",
    "new_data['BusinessTravel'] = new_data['BusinessTravel'].map({\n",
    "    'Non-Travel': 0,\n",
    "    'Travel_Rarely': 1,\n",
    "    'Travel_Frequently': 2\n",
    "})\n",
    "\n",
    "# 将'Gender'列中的值进行映射，'Female'映射为0，'Male'映射为1\n",
    "new_data['Gender'] = new_data['Gender'].map({\n",
    "    'Female': 0,\n",
    "    'Male': 1\n",
    "})\n",
    "\n",
    "# 将'MaritalStatus'列中的值进行映射\n",
    "# 'Divorced'映射为-1，'Single'映射为0，'Married'映射为1\n",
    "new_data['MaritalStatus'] = new_data['MaritalStatus'].map({\n",
    "    'Divorced': -1,\n",
    "    'Single': 0,\n",
    "    'Married': 1\n",
    "})\n",
    "\n",
    "# 将'Attrition'列中的值进行映射，'Yes'映射为1，'No'映射为0\n",
    "new_data['Attrition'] = new_data['Attrition'].map({\n",
    "    'Yes': 1,\n",
    "    'No': 0\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确定需要进行独热编码的分类变量（任职部门、专业领域）\n",
    "categorical_features = ['Department', 'EducationField']\n",
    "# 重置行索引\n",
    "new_data.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 1. 0. ... 0. 0. 0.]\n",
      " [0. 0. 1. ... 1. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " [0. 1. 0. ... 1. 0. 0.]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# 创建独热编码器\n",
    "encoder = OneHotEncoder(sparse=False, handle_unknown='ignore')\n",
    "# 应用独热编码\n",
    "encoded_data = encoder.fit_transform(new_data[categorical_features])\n",
    "print(encoded_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Department_Human Resources  Department_Research & Development  \\\n",
      "0                            0.0                                1.0   \n",
      "1                            0.0                                0.0   \n",
      "2                            0.0                                0.0   \n",
      "3                            0.0                                1.0   \n",
      "4                            0.0                                1.0   \n",
      "...                          ...                                ...   \n",
      "1411                         0.0                                1.0   \n",
      "1412                         0.0                                0.0   \n",
      "1413                         0.0                                0.0   \n",
      "1414                         0.0                                0.0   \n",
      "1415                         0.0                                1.0   \n",
      "\n",
      "      Department_Sales  EducationField_Human Resources  \\\n",
      "0                  0.0                             0.0   \n",
      "1                  1.0                             0.0   \n",
      "2                  1.0                             0.0   \n",
      "3                  0.0                             0.0   \n",
      "4                  0.0                             0.0   \n",
      "...                ...                             ...   \n",
      "1411               0.0                             0.0   \n",
      "1412               1.0                             0.0   \n",
      "1413               1.0                             0.0   \n",
      "1414               1.0                             0.0   \n",
      "1415               0.0                             0.0   \n",
      "\n",
      "      EducationField_Life Sciences  EducationField_Marketing  \\\n",
      "0                              1.0                       0.0   \n",
      "1                              0.0                       0.0   \n",
      "2                              0.0                       1.0   \n",
      "3                              1.0                       0.0   \n",
      "4                              0.0                       0.0   \n",
      "...                            ...                       ...   \n",
      "1411                           1.0                       0.0   \n",
      "1412                           0.0                       1.0   \n",
      "1413                           0.0                       1.0   \n",
      "1414                           0.0                       1.0   \n",
      "1415                           0.0                       0.0   \n",
      "\n",
      "      EducationField_Medical  EducationField_Other  \\\n",
      "0                        0.0                   0.0   \n",
      "1                        1.0                   0.0   \n",
      "2                        0.0                   0.0   \n",
      "3                        0.0                   0.0   \n",
      "4                        1.0                   0.0   \n",
      "...                      ...                   ...   \n",
      "1411                     0.0                   0.0   \n",
      "1412                     0.0                   0.0   \n",
      "1413                     0.0                   0.0   \n",
      "1414                     0.0                   0.0   \n",
      "1415                     1.0                   0.0   \n",
      "\n",
      "      EducationField_Technical Degree  \n",
      "0                                 0.0  \n",
      "1                                 0.0  \n",
      "2                                 0.0  \n",
      "3                                 0.0  \n",
      "4                                 0.0  \n",
      "...                               ...  \n",
      "1411                              0.0  \n",
      "1412                              0.0  \n",
      "1413                              0.0  \n",
      "1414                              0.0  \n",
      "1415                              0.0  \n",
      "\n",
      "[1416 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "# 将编码后的数据转换为DataFrame\n",
    "encoded_df = pd.DataFrame(encoded_data, columns=encoder.get_feature_names_out(categorical_features))\n",
    "print(encoded_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Age BusinessTravel  Education  EnvironmentSatisfaction Gender  \\\n",
      "0      18              1          3                        3      1   \n",
      "1      18              1          3                        4      0   \n",
      "2      18              2          3                        2      1   \n",
      "3      18              0          2                        2      1   \n",
      "4      18              0          1                        3      1   \n",
      "...   ...            ...        ...                      ...    ...   \n",
      "1411   60              1          3                        1      0   \n",
      "1412   60              2          3                        3      0   \n",
      "1413   60              1          4                        1      1   \n",
      "1414   60              1          4                        2      1   \n",
      "1415   60              1          4                        3      1   \n",
      "\n",
      "      JobInvolvement  JobLevel  JobSatisfaction MaritalStatus  MonthlyIncome  \\\n",
      "0                  3         1                3             0           1420   \n",
      "1                  2         1                3             0           1200   \n",
      "2                  3         1                2             0           1878   \n",
      "3                  3         1                4             0           1051   \n",
      "4                  3         1                3             0           1904   \n",
      "...              ...       ...              ...           ...            ...   \n",
      "1411               3         5                1             1          19566   \n",
      "1412               2         3                1             1          10266   \n",
      "1413               3         2                1             0           5405   \n",
      "1414               4         2                4            -1           5220   \n",
      "1415               1         3                4            -1          10883   \n",
      "\n",
      "      ...  Attrition  Department_Human Resources  \\\n",
      "0     ...          1                         0.0   \n",
      "1     ...          0                         0.0   \n",
      "2     ...          1                         0.0   \n",
      "3     ...          0                         0.0   \n",
      "4     ...          1                         0.0   \n",
      "...   ...        ...                         ...   \n",
      "1411  ...          0                         0.0   \n",
      "1412  ...          0                         0.0   \n",
      "1413  ...          0                         0.0   \n",
      "1414  ...          0                         0.0   \n",
      "1415  ...          0                         0.0   \n",
      "\n",
      "      Department_Research & Development  Department_Sales  \\\n",
      "0                                   1.0               0.0   \n",
      "1                                   0.0               1.0   \n",
      "2                                   0.0               1.0   \n",
      "3                                   1.0               0.0   \n",
      "4                                   1.0               0.0   \n",
      "...                                 ...               ...   \n",
      "1411                                1.0               0.0   \n",
      "1412                                0.0               1.0   \n",
      "1413                                0.0               1.0   \n",
      "1414                                0.0               1.0   \n",
      "1415                                1.0               0.0   \n",
      "\n",
      "      EducationField_Human Resources  EducationField_Life Sciences  \\\n",
      "0                                0.0                           1.0   \n",
      "1                                0.0                           0.0   \n",
      "2                                0.0                           0.0   \n",
      "3                                0.0                           1.0   \n",
      "4                                0.0                           0.0   \n",
      "...                              ...                           ...   \n",
      "1411                             0.0                           1.0   \n",
      "1412                             0.0                           0.0   \n",
      "1413                             0.0                           0.0   \n",
      "1414                             0.0                           0.0   \n",
      "1415                             0.0                           0.0   \n",
      "\n",
      "     EducationField_Marketing  EducationField_Medical  EducationField_Other  \\\n",
      "0                         0.0                     0.0                   0.0   \n",
      "1                         0.0                     1.0                   0.0   \n",
      "2                         1.0                     0.0                   0.0   \n",
      "3                         0.0                     0.0                   0.0   \n",
      "4                         0.0                     1.0                   0.0   \n",
      "...                       ...                     ...                   ...   \n",
      "1411                      0.0                     0.0                   0.0   \n",
      "1412                      1.0                     0.0                   0.0   \n",
      "1413                      1.0                     0.0                   0.0   \n",
      "1414                      1.0                     0.0                   0.0   \n",
      "1415                      0.0                     1.0                   0.0   \n",
      "\n",
      "      EducationField_Technical Degree  \n",
      "0                                 0.0  \n",
      "1                                 0.0  \n",
      "2                                 0.0  \n",
      "3                                 0.0  \n",
      "4                                 0.0  \n",
      "...                               ...  \n",
      "1411                              0.0  \n",
      "1412                              0.0  \n",
      "1413                              0.0  \n",
      "1414                              0.0  \n",
      "1415                              0.0  \n",
      "\n",
      "[1416 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "# 将编码后的数据与原始数据合并\n",
    "new_data = new_data.drop(categorical_features, axis=1)\n",
    "new_data = pd.concat([new_data, encoded_df], axis=1)\n",
    "print(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = new_data.drop('Attrition', axis=1)  # 特征数据\n",
    "y = new_data['Attrition']   # 目标变量\n",
    "# 采用分层抽样来保证训练集和测试集中label与整体数据集的label分布相似\n",
    "# 测试集训练集37分\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=10, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分离少数类和多数类\n",
    "# 将为1的少数类别样本分离并存储\n",
    "x_minority = x_train[y_train == 1]\n",
    "y_minority = y_train[y_train == 1]\n",
    "\n",
    "# 将0的多数类别样本分离并存储\n",
    "x_majority = x_train[y_train == 0]\n",
    "y_majority = y_train[y_train == 0]\n",
    "\n",
    "# 对少数类别的样本进行随机采样\n",
    "x_minority_resampled = resample(x_minority, replace=True, n_samples=len(x_majority), random_state=15)\n",
    "y_minority_resampled = resample(y_minority, replace=True, n_samples=len(y_majority), random_state=15)\n",
    "\n",
    "# 将过采样后的少数类别样本和原始多数类别样本合并，形成新的训练集和对应的目标变量\n",
    "new_x_train = pd.concat([x_majority, x_minority_resampled])\n",
    "new_y_train = pd.concat([y_majority, y_minority_resampled])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=15)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=15)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=15)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 建立模型\n",
    "# 随机森林分类器（RandomForestClassifier）基于新的训练集训练模型\n",
    "rf_clf = RandomForestClassifier(random_state=15)\n",
    "rf_clf.fit(new_x_train, new_y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0\n",
      " 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# 模型评估\n",
    "y_pred_rf = rf_clf.predict(x_test)\n",
    "print(y_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.97      0.91       356\n",
      "           1       0.52      0.19      0.28        69\n",
      "\n",
      "    accuracy                           0.84       425\n",
      "   macro avg       0.69      0.58      0.59       425\n",
      "weighted avg       0.80      0.84      0.81       425\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class_report_rf = classification_report(y_test, y_pred_rf)\n",
    "print(class_report_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制混淆矩阵\n",
    "cm_rf = confusion_matrix(y_test, y_pred_rf)\n",
    "# 可视化\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm_rf, annot=True, fmt='g', cmap='Blues',\n",
    "            xticklabels=['Predicted 0', 'Predicted 1'],\n",
    "            yticklabels=['Actual 0', 'Actual 1'])\n",
    "plt.title('Confusion Matrix for Random Forest Model')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VntmRI0cKAOLGjRtfPC5JUs/E5z3lbdq0EXny5FFrS+158fDwEEWKFFFrS+qJOnz4cIrj03OO0NBQYW5uLqpVq5bio+BPP1ZP6yN9TX4+AAgdHR3x77//pjgPPuv1sLS0FIMHD05x3KfSypRaz2ydOnWEubm5eP78eZpfY2qOHz+e4lOUJHXr1hWlSpUSwcHBIjg4WNy/f1+MHTtWABDNmjVTO9bV1VVYWlp+8bEWLFggAAg/Pz8hhBAVK1b86n1SU6JECdGkSZMvHqPpa1fTntnUvs9HjhxJ9bls2rSp2mtSk9dUalLrAXz37p0wMDAQjRo1UhuWldR7un79elVbUq//ypUrv/g4nz/egwcPRHBwsAgICBDr168XxsbGws7OTu1Tl9jY2BTDwp49eyYMDQ3Veu3T+x4WHx8v7O3thaurq9pxq1evTvEetmjRIgFAbNmyRdUWHx8vqlevLszMzER4eLgqDwBhZ2cnQkNDVcdOmDBBABAVKlRQe7/s3LmzMDAw+GqvedLz9Pm/pNdV0tdSrlw5tfeipE9fp0yZomrz8vISAMT48ePVHuPMmTMCgOpTpSRJn4Ymte/ZsyfVT3o/ldOGGWStq3kI7u7usLOzg5OTE9q3bw9TU1P4+fmpetE+fPiAEydOoGPHjoiIiEBISAhCQkLw/v17eHh44NGjR6rZD3bt2oUKFSqk2oMhk8kAADt37kTp0qVRqlQp1blCQkLQoEEDAMDJkyfTzOrp6YmEhATVX3uA8mKl0NBQeHp6AgCEENi1axdatGgBIYTaY3h4eCAsLAzXr19XO6+XlxeMjY2/+lxFREQAAMzNzdM8Jum28PBwtfbWrVvD0dFRtV+1alVUq1YNBw8eBKDZ85ykX79+KS40+/TrSEhIwPv371GsWDFYWVml+Lo11atXL7UeoNq1awNQ9o4AwNWrV/H+/Xv069dP7eKjrl27qvWmpCXpOfvS85uagQMHqu3Xrl0b79+/V/sefPq8hIWFISQkBHXr1sXTp08RFhamdv/ChQurevk/lZ5zHDt2DBERERg/fnyKCyiTfga+RNOfj7p166JMmTJfPa+VlRUuXbqkdrX+twoODsbp06fRu3dvFCxYUO22r32N79+/B4A0Xw/379+HnZ0d7OzsUKpUKcybNw8tW7ZMMS1YRETEV18nn/8shoeHa/zaSsr6tenfvvW1m16pfZ8bNGgAW1tbeHt7q9o+fvyIY8eOqd4Pge97z03L8ePHER8fjxEjRqhdpNuvXz9YWFjgwIEDascbGhqiV69eGj1GyZIlYWdnB2dnZ/Tu3RvFihXDoUOHVD2GSedNeny5XI7379/DzMwMJUuWTPX9Lj3vYe/evcPAgQPVjuvZsycsLS3VznXw4EHkzZsXnTt3VrXp6+tj2LBhiIyMxN9//612fIcOHdTOkXSRVrdu3dTeL6tVq4b4+Ph0zyq0a9cuHDt2TPVv69atal/LTz/9pPZe1KxZM5QqVSrF9wgABg0apLa/c+dOWFpa4scff1R77bi5ucHMzEz12knq4d2/fz8SEhLSlTu74zCDLGbZsmUoUaIEwsLCsH79epw+fRqGhoaq2x8/fgwhBCZPnozJkyeneo53797B0dERT548Qbt27b74eI8ePcK9e/dgZ2eX5rnSUqFCBZQqVQre3t7o06cPAOUQA1tbW9Ubc3BwMEJDQ7F69eo0p5D5/DEKFy78xcxJkn5RRUREpDnXYloFb/HixVMcW6JECfj4+ADQ7Hn+Uu6YmBjMmTMHGzZswKtXr9SmCvu8aNPU54VLUkHy8eNHAFDNGfr5Fc96enppfvz9KQsLCwDJz6E2ciWd89y5c5g6dSouXLiA6OhotePDwsLUfsmk9XpIzzmePHkCAChXrpxGX0MSTX8+0vva/e233+Dl5QUnJye4ubmhadOm6NGjB4oUKaJxxqRf/N/6NQJIcwo7Z2dnrFmzBgqFAk+ePMGsWbMQHByc4g8Dc3PzrxaYn/8sWlhYqLJrmvVrRfq3vnbTK7Xvs56eHtq1a4dt27YhLi4OhoaG2L17NxISEtSK2e95z01L0s960hCcJAYGBihSpEiK+YMdHR3THL6Vll27dsHCwgLBwcFYsmQJnj17lqLTIWmWheXLl+PZs2eQy+Wq2/LkyZPinOl9D/v8/VpfXz/Fz8rz589RvHjxFDPuJA3X+vw5+Pyxk95zPh268Gl7UqavqVOnDmxtbVO0p/U9AoBSpUqlmBVCT08vxVCwR48eISwsDPb29qk+dtJrp27dumjXrh2mTZuGhQsXol69emjdujW6dOmiVk/kJCxms5iqVauicuXKAJS9h7Vq1UKXLl3w4MEDmJmZqeZ3HDNmTKq9VUDK4uVLFAoFXFxcsGDBglRv//wH+3Oenp6YNWsWQkJCYG5uDj8/P3Tu3Fn1l21S3m7duqUYW5vk83E66emVBZRvUnv37sU///yDOnXqpHrMP//8AwDp6i371Lc8z6nlHjp0KDZs2IARI0agevXqsLS0hEwmQ6dOndKcqzO90ppuLK3CRFOlSpUCANy+fRuurq7pvt/Xcj158gQNGzZEqVKlsGDBAjg5OcHAwAAHDx7EwoULUzwvqT2vmp7jW2n685He127Hjh1Ru3Zt7NmzB0ePHsW8efMwd+5c7N69G02aNPnu3OmVVGCk9Yva1NRUbax5zZo1UalSJUycOBFLlixRtZcuXRo3b95EYGBgiiIhyec/i6VKlcKNGzfw4sWLr77PfOrjx4+p/jH6KU1fu2kVx58WY59K6/vcqVMnrFq1CocOHULr1q3h4+ODUqVKoUKFCqpjvvc9VxvS+zr91KdFWosWLeDi4oKuXbvi2rVrqgJy9uzZmDx5Mnr37o0ZM2bAxsYGOjo6GDFiRKo/kxn9HvYlaT22lJk+9WkvdxKFQgF7e3tVb+/nkv5Akslk8PX1xcWLF7Fv3z4cOXIEvXv3xvz583Hx4sUvjr/PrljMZmG6urqYM2cO6tevj6VLl2L8+PGqv0b19fXVfsmkpmjRorhz585Xj7l16xYaNmyYro9dP+fp6Ylp06Zh165dcHBwQHh4uOpCB0D5w2Vubg65XP7VvJpq3rw55syZg02bNqVazMrlcmzbtg3W1taoWbOm2m2PHj1KcfzDhw9VPZaaPM9f4uvrCy8vL8yfP1/VFhsbm2Ky8W957r8maQL8x48fo379+qr2xMREBAQEfHWwf5MmTaCrq4stW7Zo9UKaffv2IS4uDn5+fmqFjyYfr6b3HEWLFgUA3Llz54t/5KX1/H/vz8eX5MuXDz/99BN++uknvHv3DpUqVcKsWbNUxWx6Hy/ptfq1n/XUJBV9z549S9fx5cuXR7du3bBq1SqMGTNG9dw3b94c27dvx6ZNm/C///0vxf3Cw8Px119/oVSpUqrvQ4sWLbB9+3Zs2bIFEyZMSNfjJyYm4sWLF2jZsuUXj9P0tWttbZ3qAgCarohWp04d5MuXD97e3qhVqxZOnDiBSZMmqR2TEa+ppJ/1Bw8eqPVYxsfH49mzZ1p/7zUzM8PUqVPRq1cv+Pj4qN7zfX19Ub9+faxbt07t+NDQ0FR7K78m6et69OiR6tM+QDlk69mzZ2p/JBQqVAj//PMPFAqFWhF4//59tXNJ5dPv0adfS1JbevIVLVoUx48fR82aNdP1B8kPP/yAH374AbNmzcK2bdvQtWtX7NixA3379s2Q3zlS4pjZLK5evXqoWrUqFi1ahNjYWNjb26NevXpYtWoV3rx5k+L44OBg1Xa7du1w69Yt7NmzJ8VxSX9lduzYEa9evcKaNWtSHBMTE6O6Kj8tpUuXhouLC7y9veHt7Y18+fKpFZa6urpo164ddu3aleov20/zaqpGjRpwd3fHhg0bUl1haNKkSXj48CF+/vnnFD/4e/fuVRsDdfnyZVy6dElVSGjyPH+Jrq5uir/o//jjjxQ9PqampgCg1RV1KleujDx58mDNmjVITExUtW/dujVdH5k5OTmhX79+OHr0KP74448UtysUCsyfPx8vX77UKFdSz8fnQy42bNig9XM0atQI5ubmmDNnDmJjY9Vu+/S+pqamqQ77+N6fj9TI5fIUj2Vvb4/8+fMjLi7uq5k+Z2dnhzp16mD9+vUIDAxUu+1rvUmOjo5wcnLSaDWsn3/+GQkJCWo9i+3bt0eZMmXw66+/pjiXQqHAoEGD8PHjR0ydOlXtPi4uLpg1axYuXLiQ4nEiIiJSFIJ3795FbGwsatSo8cWMmr52ixYtirCwMFXvMQC8efMm1ffOL9HR0UH79u2xb98+bN68GYmJiWpDDICMeU25u7vDwMAAS5YsUfuer1u3DmFhYWjWrJnG5/yarl27okCBAmqzlKT2frdz585vXsWycuXKsLOzw8qVKxEfH69q37hxY4r3yqZNmyIoKEhtzHJiYiL++OMPmJmZoW7dut+UQVsqV64Me3t7rFy5Uu3n/NChQ7h37166vkcdO3aEXC7HjBkzUtyWmJioek4+fvyY4vuQ9AlF0mMnjXXOKau4sWc2Gxg7diw6dOiAjRs3YuDAgVi2bBlq1aoFFxcX9OvXD0WKFMHbt29x4cIFvHz5UrXc49ixY+Hr64sOHTqgd+/ecHNzw4cPH+Dn54eVK1eiQoUK6N69O3x8fDBw4ECcPHkSNWvWhFwux/379+Hj44MjR46ohj2kxdPTE1OmTIGRkRH69OmT4qORX3/9FSdPnkS1atXQr18/lClTBh8+fMD169dx/PjxFFOZaGLTpk1o2LAhWrVqhS5duqB27dqIi4vD7t27cerUKXh6emLs2LEp7lesWDHUqlULgwYNQlxcHBYtWoQ8efLg559/Vh2T3uf5S5o3b47NmzfD0tISZcqUwYULF3D8+PEU48dcXV2hq6uLuXPnIiwsDIaGhmjQoEGaY6PSw8DAAL/88guGDh2KBg0aoGPHjggICMDGjRtRtGjRdP1lPn/+fDx58gTDhg3D7t270bx5c1hbWyMwMBA7d+7E/fv31Xri06NRo0YwMDBAixYtMGDAAERGRmLNmjWwt7dP9Q+H7zmHhYUFFi5ciL59+6JKlSro0qULrK2tcevWLURHR+PPP/8EALi5ucHb2xujRo1ClSpVYGZmhhYtWmjl5+NzERERKFCgANq3b69awvX48eO4cuWKWg9+WplSs2TJEtSqVQuVKlVC//79UbhwYQQEBODAgQO4efPmF/O0atUKe/bsSddYVEA5TKBp06ZYu3YtJk+ejDx58sDAwAC+vr5o2LAhatWqpbYC2LZt23D9+nWMHj1a7bWir6+P3bt3w93dHXXq1EHHjh1Rs2ZN6Ovr499//1V9qvLp1GLHjh2DiYkJfvzxx6/m1OS126lTJ4wbNw5t2rTBsGHDEB0djRUrVqBEiRIaX6jp6emJP/74A1OnToWLi0uKKfYy4jVlZ2eHCRMmYNq0aWjcuDFatmyJBw8eYPny5ahSpQq6deum0fnSQ19fH8OHD8fYsWNx+PBhNG7cGM2bN8f06dPRq1cv1KhRA7dv38bWrVu/aSx40mPMnDkTAwYMQIMGDeDp6Ylnz55hw4YNKc7Zv39/rFq1Cj179sS1a9fg7OwMX19fnDt3DosWLcqwiwHTS19fH3PnzkWvXr1Qt25ddO7cWTU1l7OzM0aOHPnVc9StWxcDBgzAnDlzcPPmTTRq1Aj6+vp49OgRdu7cicWLF6N9+/b4888/sXz5crRp0wZFixZFREQE1qxZAwsLCzRt2hSAcqhJmTJl4O3tjRIlSsDGxgblypX7rrH3ksrUuRMoTWktmiCEcpLkokWLiqJFi6qmfnry5Ino0aOHyJs3r9DX1xeOjo6iefPmwtfXV+2+79+/F0OGDBGOjo6qybm9vLzUpsmKj48Xc+fOFWXLlhWGhobC2tpauLm5iWnTpomwsDDVcZ9PzZXk0aNHqmlIzp49m+rX9/btWzF48GDh5OQk9PX1Rd68eUXDhg3F6tWrVcckTdeyc+dOjZ67iIgI8csvv4iyZcsKY2NjYW5uLmrWrCk2btyYYmqiTyfMnj9/vnBychKGhoaidu3a4tatWynOnZ7n+Uvfu48fP4pevXoJW1tbYWZmJjw8PMT9+/dTfS7XrFkjihQpInR1ddWm6Upraq7Pn6e0JtNfsmSJKFSokDA0NBRVq1YV586dE25ubqJx48bpeHaVqyWtXbtW1K5dW1haWgp9fX1RqFAh0atXL7Wpj9KaNDzp+fl0oQg/Pz9Rvnx51WTec+fOFevXr09xXNKiCalJ7zmSjq1Ro4YwNjYWFhYWomrVqmL79u2q2yMjI0WXLl2ElZWV2nQ6QqT/5wP/TaafGnwyBU5cXJwYO3asqFChgjA3NxempqaiQoUKKRZ8SCtTWt/nO3fuiDZt2ggrKythZGQkSpYsKSZPnpxqnk9dv35dAEgxVVRaiyYIIcSpU6dSndbn3bt3YtSoUaJYsWLC0NBQWFlZCXd3d9V0XKn5+PGjmDJlinBxcREmJibCyMhIlCtXTkyYMEG8efNG7dhq1aqJbt26ffVrSpLe164QQhw9elSUK1dOGBgYiJIlS4otW7Z8cdGEtCgUCuHk5CQAiJkzZ6Z6THpfU6n50uT8S5cuFaVKlRL6+vrCwcFBDBo0KM1FE9LrS48XFhYmLC0tVe9PsbGxYvTo0SJfvnzC2NhY1KxZU1y4cOG738OWL18uChcuLAwNDUXlypW/uGhC0vutgYGBcHFxSXGuT38HfCqtTF96f0/v8/Qpb29vUbFiRWFoaChsbGy+uGhCWlavXi3c3NxUv+9cXFzEzz//rFrI5Pr166Jz586iYMGCqoUVmjdvLq5evap2nvPnzws3NzdhYGCQ7afpkgmRyaOaiSQUEBCAwoULY968eRgzZozUcSShUChgZ2eHtm3bpvpRJ+U+DRs2RP78+bF582apo6Tp5s2bqFSpEq5fv67RBYlElPNxzCxRDhYbG5ti7NSmTZvw4cMH1KtXT5pQlOXMnj0b3t7eGl/wlJl+/fVXtG/fnoUsEaXAMbNEOdjFixcxcuRIdOjQAXny5MH169exbt06lCtXDh06dJA6HmURSRPDZ2U7duyQOgIRZVEsZolyMGdnZzg5OWHJkiX48OEDbGxs0KNHD/z6668aT5pORESUFXHMLBERERFlWxwzS0RERETZFotZIiIiIsq2ct2YWYVCgdevX8Pc3DzHLedGRERElBMIIRAREYH8+fOnWIzpc7mumH39+jWcnJykjkFEREREX/HixQsUKFDgi8fkumI2aUm7Fy9ewMLCQuI0RERERPS58PBwODk5pWsp4lxXzCYNLbCwsGAxS0RERJSFpWdIKC8AIyIiIqJsi8UsEREREWVbLGaJiIiIKNtiMUtERERE2RaLWSIiIiLKtljMEhEREVG2xWKWiIiIiLItFrNERERElG2xmCUiIiKibIvFLBERERFlWyxmiYiIiCjbYjFLRERERNkWi1kiIiIiyrZYzBIRERFRtiVpMXv69Gm0aNEC+fPnh0wmw969e796n1OnTqFSpUowNDREsWLFsHHjxgzPSURERERZk6TFbFRUFCpUqIBly5al6/hnz56hWbNmqF+/Pm7evIkRI0agb9++OHLkSAYnJSIiIqKsSE/KB2/SpAmaNGmS7uNXrlyJwoULY/78+QCA0qVL4+zZs1i4cCE8PDwyKiYRERFJ6dlh4P42IDFG6iRUshNQop3UKdRIWsxq6sKFC3B3d1dr8/DwwIgRI9K8T1xcHOLi4lT74eHhGRWPiIiItCk8EDg5HHi8V+okudbjEBsM8G2ONR32oUiej4BdBakjpZCtLgALCgqCg4ODWpuDgwPCw8MRE5P6X2tz5syBpaWl6p+Tk1NmRCUiIqJvJY8HLv0KbCjNQlZCPjfLotLCATjxuAg6bWmP+ERdqSOlKlv1zH6LCRMmYNSoUar98PBwFrRERERZVeBJwP8n4MP95DYTB6Du74BTPcli5SYxMYkYOeESVm1J/h6EGpTGm+a3UaiYo4TJUpetitm8efPi7du3am1v376FhYUFjI2NU72PoaEhDA0NMyMeERERfavIN8DfY5RjY5PIdADXIUDN6YChpXTZcpEHD0LQsaMv/vknud7q0sUFK1c2g7l51qynslUxW716dRw8eFCt7dixY6hevbpEiYiIiOi7KBKBm8uAc1OA+E+ua8n3A9BwOeBQUbpsuczWrf9gwID9iIpKAAAYGelh6dIm6N27ImQymcTp0iZpMRsZGYnHjx+r9p89e4abN2/CxsYGBQsWxIQJE/Dq1Sts2rQJADBw4EAsXboUP//8M3r37o0TJ07Ax8cHBw4ckOpLICIiom/1+gJwfBAQfCu5zcgGqD0XcOmt7JmlDBcdnYBhww5h3bobqrZSpWyxc2cHlCtnL2Gy9JG0mL169Srq16+v2k8a2+rl5YWNGzfizZs3CAwMVN1euHBhHDhwACNHjsTixYtRoEABrF27ltNyERERZSfRIcCZccCd9ertLn2BWnMAE1tpcuVSly69VCtkvbwqYNmypjA1NZAwVfrJhBBC6hCZKTw8HJaWlggLC4OFhYXUcYiIiHIPoQBurwXOTABiPyS327kC7iuA/D9IFi23Gz/+OP744zKWL28KLy9XqeNoVK+xmCUiIqKM9/a6cpaCN5eS2wwsgJozAddBgE62uownW4uJSYCRkZ7aONiEBDkCAkJRvHgeCZMl06Re42AUIiIiyjixoYD/EGBrFfVCtnRXoPcDoNJQFrKZ6Pbtt6hUaTVWrLiq1q6vr5tlCllN8dVDRERE2icEcG+Lcrqt6HfJ7TalAfflnDM2kwkhsHbtdQwbdhixsYkYOfIIqlcvgIoV80kd7buxmCUiIiLtCvlXOaTg5enkNj0ToPpUwG0EoJs9LizKKSIi4jBgwH5s335H1Va6tC3MzHLG94HFLBEREWlHfCRwYRpwfZFy/tgkxdsC9RYCFgUli5Zb3bjxBh07+uLx4+QL7n76qTLmz/eAkVHOKANzxldBRERE0hECeLQLODkSiHyZ3G5VFGjwB1C4iXTZcikhBFasuIpRo44gLk4OALCwMMTatS3QoUNZidNpF4tZIiIi+nYfHykv8Hp+NLlN1xCoOgGoOg7QM5IuWy4VFhaLvn33wdf3rqrNzS0fvL3bo2hRGwmTZQwWs0RERKS5hBjg8hzgylxAHp/c7twYaLhU2StLkhACuHr1tWp/2LCq+O23H2FomDPLvpz5VREREVHGeXoAODEUCHuW3GbuBNRfBBRrA3wyfyllPisrI3h7t0eLFtuxalVztG5dSupIGYrFLBERUU4lFMrFCt7dAIRcO+d8dhh48lfyvo4e4DYaqD4Z0DfVzmOQRj5+jEFcnBx585qp2qpWdcSzZ8NhYqIvYbLMwWKWiIgoJ4l4pRy/GnAUeH4MiH2fcY/lVA9ouAzIUybjHoO+6OLFl+jUyRfOzlY4frwH9PSS18PKDYUswGKWiIgoe0uIVs7nmlTAvv834x/TxAGoNx8o1YVDCiSiUAgsWHABEyb4IzFRgefPwzB37llMmlRH6miZjsUsERFRdiIEEPxPcvH66gwgj0v9WAMLoGBDwKk+YPjl9e3TTd8UKNRIe+cjjYWERKNnz704cOCRqq1mTSf06FFBwlTSYTFLRESU1UW9VQ4ZeP7f0IGooNSPk+kAeasCzh7KgjNfVeWYVsoxzp4NROfOu/DyZbiqbfz4mpg+vT709XUlTCYdvsKJiIiymsQ44NXZ5N7X4JtpH2teUFm8OnsABRsARtaZFpMyj0IhMHfuWUyefBJyuQAA2NqaYPPmNmjcuJjE6aTFYpaIiCgriA4B7m1RFrAvTgGJMakfp2+qHDaQ1PtqXZzjVnO4+Hg5WrbcjiNHnqja6tYthG3b2iF/fnMJk2UNLGaJiIikFh0MbK6kvhTspxzclIWrsweQvzqga5C5+UhSBga6KFzYCoDy75b//a8OpkypqzZzQW7GYpaIiEhqp8epF7Jm+ZXFa6FGQCF3wMROumyUJSxc2BjPnoVizJgacHcvInWcLIXFLBERkZRenQP+3aDcNrQEOpwE7F05dCAXCwqKxD//vEWjRslLAhsZ6eHw4W4Spsq62D9NREQkFUUi4P9T8n7NWYBDRRayudjx40/h6roSbdt64/79EKnjZAssZomIiKRyY6lyzlgAsK8EVBgobR6STGKiApMnn0CjRpvx9m0UoqISMGLEYaljZQscZkBERCSFyNfA+Sn/7cgA9+WATu6cJzS3e/UqHF267Mbp089VbY0bF8OmTa2lC5WNsJglIiKSwqnRQHyEcrt8PyBfNWnzkCQOH36M7t33ICQkGgCgqyvDrFkNMHZsTejocLhJerCYJSIiymzP/YEHO5TbRnmAWrOlzUOZLiFBjsmTT2Lu3HOqtgIFLLBjRzvUrFlQwmTZD4tZIiKizJQYB/gPTt6vMxcwziNdHpJEly674et7V7XfvHkJbNzYCnnymEiYKnviBWBERESZ6doC4OMD5Xa+6kC5XtLmIUn89FNl6OjIoKeng99//xF+fp1YyH4j9swSERFllvDnwMUZym2ZjvKiLxn7lXKj+vULY/HixqhcOT9++KGA1HGyNf4EERERZZYTw4HEGOV2xaHKxREoxwsICMX48cehUAi19iFDqrKQ1QL2zBIREWWGpweAJ38pt03zAjWmSZuHMsWePffQu7cfQkNjkSePMcaOrSl1pByHPbNEREQZLSEGODE0eb/ufOXStZRjxcUlYtiwQ2jb1gehobEAgHXrbiAuLlHiZDkPe2aJiIgy2uU5QNgz5bZTfaBUZ2nzUIZ68uQDPD19ce3aG1Vbhw5lsGZNCxgasvTSNj6jREREGenjI+DKXOW2jj7QcBkg42T4OdXOnf+ib999CA+PAwAYGupi4UIPDBxYGTJ+3zMEi1kiIqKMIgTgPwSQxyv33UYBeUpLm4kyRGxsIkaNOoIVK66q2ooXt4GPTwe4uuaVMFnOx2KWiIgoozzaBTw/qtw2dwKqT5Y2D2WYWbNOqxWyXbq4YOXKZjA3N5QwVe7AC8CIiIgyQsgd4MSw5P36iwF9U+nyUIb6+eeaKFEiD4yM9LBmTQts2dKGhWwmYc8sERGRtt3dAhwbACRGK/cLNwWKtZY0EmUsc3ND+Pp2AAC4uDhInCZ3Yc8sERGRtiTGAccHAYe6JxeydhWARmt40VcOcu9eMOrU2YCAgFC1dhcXBxayEmAxS0REpA1hAcCOWsCtlclt5XoDnS8AZvkli0Xa9eefN1G58hqcORMIT09fxMfLpY6U63GYARER0fd6ehA41A2I/ajc1zMCGiwDXHpLm4u0JioqHoMHH8Sff95StUVHJyA4OAqOjhYSJiMWs0RERN9KIQfOTwUuzUpusyoKtPAF7F0li0Xadfv2W3Ts6Iv790NUbX37VsTixU1gYqIvYTICWMwSERF9m+h3wIEuQKB/clux1oDHBsDISqpUpEVCCKxbdwNDhx5CbKxyGVozMwOsWtUcXbq4SJyOkrCYJSIi0tSr88D+jkDkK+W+TBeoPQeoPIYXeuUQERFxGDjwALZtu61qq1DBAT4+HVCiRB4Jk9HnWMwSERGllxDA9cXA6bGAQtlTB9O8QHNvoEAdabORVl248FKtkB040A0LFzaGkRFLp6yGsxkQERGlR1y4sjf21MjkQrZAXaD7DRayOVCjRkUxenR1mJsbwNu7PVasaM5CNouSCSGE1CEyU3h4OCwtLREWFgYLC159SERE6RB8G9jXHvj4MLmtyjig1kxAhwVOThAVFQ8TE33IPhkmEh8vx6tX4Shc2FrCZLmTJvUae2aJiIi+5O5mYFu15ELW0BJo9RdQ51cWsjnE1auvUb78SqxefU2t3cBAl4VsNsBiloiIKDWJscCxgcChHkBijLLNzhXodg0o1lLSaKQdQggsWXIJNWqsw9OnHzF8+GHcuhUkdSzSEP+kJCIi+lxYgHJYwdtPeupc+gL1lwD6xpLFIu35+DEGffr4Yc+e+6q2ChXywtLSSMJU9C1YzBIREX3q6QHgUHf11bwargDK9ZQ0FmnPpUsv4enpi+fPw1Rto0dXx+zZDWFgoCthMvoWLGaJiIiANFbzKvbfal4VpMtFWiOEwIIFFzB+vD8SExUAABsbY2zc2AotWpSUOB19KxazRERE0e+AA52BwBPJbcXaAI03KC/4omzvw4cYeHntxf79yTNS1KzphO3b28HJid/j7IzFLBER5W6vzv23mtdr5b5MF6gzF3AbxdW8cph//nmr2h4/viamT68PfX0OK8juWMwSEVHWJhTAld+BR7sAIdfyuQUQ8s8nq3nl+281r9rafRySnI2NMby926NtW2+sX98KjRsXkzoSaQmLWSIiyroUicCRPsDdTRn/WE71gGbblcvTUrYXHBwFhULAwcFM1fbDDwXw9OlwruSVw/C7SUREWZM8ATjYDXjok9yWEYsUGJgDroOB6lO5CEIOcfr0c3TuvAslS+bBsWPdoaubPK0+C9mch99RIiLKehJjgf2ewBM/5b6OvvLj/+JtpM1FWZpcrsCcOWcxdeopKBQCr19H4Pffz2PcuFpSR6MMxGKWiIiyloRo4K82wPOjyn09I6DlbqBwE2lzUZYWFBSJbt12w9//maqtQYPC8PJylS4UZQoWs0RElHXERwB7mgMvTyv39U2B1n5AwQbS5qIszd//Kbp23Y23b6MAADo6MvzyS11MnFhbbYgB5UwsZomIKGuIDQV2NwbeXFLuG1gAbQ8CjjUljUVZl1yuwPTpf2PGjNMQQtmWL58Ztm1rh3r1nCXNRpmHxSwREUkvOgTY1Qh4d0O5b2QNtDsK5K0sbS7KsmJjE9G48Rb8/fdzVVujRkWxeXMb2NubSpiMMhv73omISFqRbwCfesmFrLEd0PEUC1n6IiMjPZQokQcAoKsrw5w5DXHoUFcWsrkQe2aJiEg64S8A34bAx0fKfbP8QHt/IE8paXNRtrB4cWO8ehWBCRNqoVatglLHIYmwmCUiImmEPgV2NgTCA5T7FoWADv6AVVFJY1HW9OJFGO7dC0GjRsmvD2NjfRw40EXCVJQVcJgBERFlvvf3Ae86yYWsVTHA8zQLWUrVgQMP4eq6Cu3a+eDhw/dSx6EshsUsERFlruDbgE9dIPKVcj9PGWUha8GPiUldQoIcY8YcRfPm2/HhQwwiI+MxduwxqWNRFsNhBkRElHneXgN8GwGxH5T7dq5A+6OAiZ2ksSjrCQgIRadOvrh06ZWqrXXrUli/vqWEqSgrYjFLRESZ49V5YHcTID5cuZ+3KtDusHIaLqJP7N17H716/YXQ0FgAgL6+Dn7/vRGGDq0KmUwmcTrKaljMEhFRxgs8CextASQoV2iCY22gzX7A0ELaXJSlxMUlYty441i8+JKqrUgRa3h7t0flyvklTEZZGYtZIiLKWM8OA35tgERlLxsK/Qi02gvom0gai7Ke9u13Yv/+h5/sl8HatS1gaWkkYSrK6ngBGBERZZxHe4C9LZML2SLNgdZ+LGQpVSNGVINMBhga6mL58qbw8WnPQpa+ij2zRESkffGRwL9/AieHA0KubCvRHmi6FdA1kDYbZVkNGxbBH380Qc2aBeHqmlfqOJRNsJglIiLtkMcrhxTc3w488QMSo5NvK9Md8FgP6PDXDik9evQea9Zcx9y57moXdQ0eXFXCVJQd8V2FiIi+nUIOvDwN3N8GPNoFxH5MeUz5AYD7ckDGkW2ktH37bfTvvx+RkfHIl88MI0dWlzoSZWOSv7MsW7YMzs7OMDIyQrVq1XD58uUvHr9o0SKULFkSxsbGcHJywsiRIxEbG5tJaYmICEIAQVeAU6OA1U7AzgbA7bXqhayRjbKI9TwN/LiShSwBAGJiEtCvnx+6dNmNyMh4AMDGjbeQkCCXOBllZ5L2zHp7e2PUqFFYuXIlqlWrhkWLFsHDwwMPHjyAvb19iuO3bduG8ePHY/369ahRowYePnyInj17QiaTYcGCBRJ8BUREucj7e8ohBPe3A6GPU96ubwoUaw2U6qycsYBjY+kT9+4Fo2NHX9y5807V1qNHBSxb1hT6+roSJqPsTiaEEFI9eLVq1VClShUsXboUAKBQKODk5IShQ4di/PjxKY4fMmQI7t27B39/f1Xb6NGjcenSJZw9ezZdjxkeHg5LS0uEhYXBwoLzGxIRfVH4C+DBDuDeNiD4ZsrbdfSBwk2AUl2Aos2VBS3RZzZtuoVBgw4gOjoBAGBioo9ly5qiZ09XaYNRlqVJvSZZz2x8fDyuXbuGCRMmqNp0dHTg7u6OCxcupHqfGjVqYMuWLbh8+TKqVq2Kp0+f4uDBg+jevXuajxMXF4e4uDjVfnh4uPa+CCKirCbyNfB4L5AY833nkccpL+Z6dSaVG2VAwfpAyc5AiXZcwYvSFBUVjyFDDmHjxpuqtrJl7eDj0wFlynAJY9IOyYrZkJAQyOVyODg4qLU7ODjg/v37qd6nS5cuCAkJQa1atSCEQGJiIgYOHIiJEyem+Thz5szBtGnTtJqdiChLCjwJ+LUF4kIz5vx5qyqHEJTsCJhxNSb6uunT/1YrZPv0qYglS5rAxERfulCU42SrEfmnTp3C7NmzsXz5cly/fh27d+/GgQMHMGPGjDTvM2HCBISFhan+vXjxIhMTExFlkjsbgF2NtF/I2pQGas4Aej8Cul4C3EawkKV0mzSpDooVs4GpqT62bGmDtWtbspAlrZOsZ9bW1ha6urp4+/atWvvbt2+RN2/qEyVPnjwZ3bt3R9++fQEALi4uiIqKQv/+/TFp0iTo6KSszQ0NDWFoaKj9L4CIKCsQCuDs/4DLc5LbCjcFyvX6/nNblwRsywGfzAFK9CVCCLU5Yy0sDLF7d0cYGOiiZElbCZNRTiZZMWtgYAA3Nzf4+/ujdevWAJQXgPn7+2PIkCGp3ic6OjpFwaqrq7wCUsLr2IiIpJEQAxzuCTz0SW6rOBSot4CLE1Cmu3UrCAMHHoC3d3sULGipandxcfjCvYi+n6TvdqNGjYKXlxcqV66MqlWrYtGiRYiKikKvXsoehR49esDR0RFz5ih7HFq0aIEFCxagYsWKqFatGh4/fozJkyejRYsWqqKWiChXiHoL/NUKeHNJuS/TAeotAioNlTQW5T5CCKxadQ0jRhxGXJwcnTvvwqlTXpxuizKNpMWsp6cngoODMWXKFAQFBcHV1RWHDx9WXRQWGBio1hP7v//9DzKZDP/73//w6tUr2NnZoUWLFpg1a5ZUXwIRUeZ7fxfY3QwID1Du65sBzXcARZpJGotyn7CwWPTvvx8+Pv+q2mJjE/HhQwwcHMwkTEa5iaTzzEqB88wSUbYWcAzY1x6I/2+aQTNHoM0BwL6CtLko17l27TU8PX3x5Enyym9Dh1bFvHk/wtCQw1zo+2SLeWaJiEhD/6wBjg8CxH9Lf9pXAtrs4+wClKmEEFi69DLGjDmG+Hjla9HKygjr17dEmzalJU5HuRGLWSKirE4ogNPjgKu/J7cVbQk028YVtyhTffwYgz59/LBnT/J88FWrOsLbuz2cna2kC0a5GotZIqKsLCEaONgNeLwnuc1tFFDnN0CHF9hQ5jp//oVaITt6dHXMnt0QBgZ8LZJ0stWiCUREuUrkG8C7bnIhK9MFGi4H6s1nIUuSaNasBIYPrwYbG2P4+XXC7783YiFLkuMFYEREWVHwbWBPMyDiv1ULDcyBFjsBZw9pc1GuEhERBzMzA7WFEOLj5Xj3LgoFCvB3KGUcTeo19swSEWU1972B7dWTC1nzgkDn8yxkKVOdP/8CZcsux/r1N9TaDQx0WchSlsJilogoq5DHAyeGAQc6AQlRyra8VYCul5TLyhJlAoVCYO7cs6hTZwNevAjH0KGHcOfOO6ljEaWJF4AREWUF4S+A/R2SV/QCgDI9APcVgL6JdLkoVwkOjkKPHntx+PBjVVvlyvlhbW0kYSqiL2MxS0QktYCjwIEuQOx75b6uIdDgD8ClL/DJWEWijHT69HN07rwLr19HAFC+9CZNqo2pU+tBT48f5FLWxWKWiEgqQgFcmAFcmAbgv2txLZyBlr6Ag5uUySgXkcsVmDPnLKZOPQWFQvk6tLc3xdatbeHuXkTidERfx2KWiEgK0SHAoW5AwJHktiLNgMabAGMb6XJRrvLuXRS6dt2N48efqtoaNCiMLVvaIF8+cwmTEaUfi1kiosz25hKwr0PybAUyHaDmTKDqOOU2USbR1ZXh/v0QAICOjgxTp9bFpEm1oavL1yFlHyxmiYgyixDAzWXAqVGAIkHZZmIPNNsOFGwgbTbKlfLkMcH27e3QpcsubNrUBvXqOUsdiUhjLGaJiDJDfCRwtB/wYEdyW/6aQHNvwNxRulyUq7x+HQE9PR3Y25uq2mrVKohHj4bC0JAlAWVP/ByBiCijvb8HbK2qXsi6jQI6nmQhS5nm6NEncHVdiW7ddqsu9ErCQpayMxazREQZ6d52YGsV4MM95b6BOdByF1BvPqCrL202yhUSExWYONEfHh5bEBwcjWPHnmLRootSxyLSGv4pRkSUERLjgL9HK8fIJrF1URay1sWly0W5ysuX4ejceRfOng1UtTVtWhw9elSQMBWRdrGYJSLStvBA5WwFQZeT28p6AQ2XczUvyjQHDjyEl9devH8fAwDQ09PBnDkNMWpUdejocDEOyjlYzBIRaVPAEeBAV67mRZJJSJBj4kR//P77BVVbwYKW2LGjHapXd5IwGVHGYDFLRKQNCjlwYTpwcQa4mhdJJTo6AQ0bbsLFiy9Vba1alcT69a1gY2MsYTKijMMLwIiIvld0CLC7KXBxOlSFbJHmQPfrLGQpU5mY6KN0aVsAgL6+DhYt8sCePZ4sZClHY88sEdH3eH1ROT428r+eMK7mRRJburQpgoOjMWVKHVSpwqnfKOdjMUtEuZdQKP99030FcGulcsYCtdW8dgAF62svI9EXPH36EY8evYeHRzFVm4mJPvbt6yxhKqLMxWKWiHIPIYAPD4Cn+4FnB4BXZwFFonbO7VhLuZqXWX7tnI/oK3x976JPHz8IIXD9+gAUK2YjdSQiSbCYJaKcLTEOeHk6uYANfaL9x3AbDdSew0UQKFPExiZi9OgjWL78qqptwgR/7NzZQcJURNJhMUtEOU9UEPD0oLKAfX4MSIhM/TjLIt/Xk2pgDpQfCBRr+e3nINLAo0fv4enpixs3glRtnTqVw6pVzSVMRSQtFrNElP0JBfDuBvDkv97XoCupH6ejBxSoAxRuppxtwKZE5uYk+g47dtxBv377EBkZDwAwMtLDkiWN0bdvJcg4hzHlYixmiSh7io8Enh//b/jAQSDqTerHGdsChZsqi1fnRoChZebmJPpOMTEJGDHiMFavvq5qK1kyD3x8OqB8eQcJkxFlDSxmiSj7CH0KPD2gLGBfngLk8akfZ+cKFPmv9zVvFUBHNzNTEmlVy5Y7cPz4U9V+9+7lsXx5M5iZGUiYiijrYDFLRFlfeCCwvxPw5kLqt+sZAwUbKovXwk0BCy7ZSTnHmDHVcfz4Uxgb62H58mbo2dNV6khEWQqLWSLK2j4+BnY2BCIC1dvNCyb3vjrVB/S5whHlTB4exbB0aRPUr18YZcrYSR2HKMthMUtEWdf7e8pCNmk8rGVhwKWfsoC1LQfwohfKYf799x02bLiJefN+VLuoa/DgqhKmIsraWMwSUdb07hbg+yMQE6zct3UB2h8DTHnBC+U8Qghs2HATQ4YcRExMIgoWtMSwYdWkjkWULXDhcCLKet5cBnbWTy5k7SsBHU+ykKUcKTIyHj167EWfPn6IiVGuSLd58z+Qy79xqWWiXIY9s0SUtbw8C+xpCsRHKPfzVQfaHgSMrCSNRZQRbt0KQseOvnj48L2qbcAANyxc6AFdXfY3EaUHi1kiyjqeHwf2tgISo5X7TvWA1vsAAzNJYxFpmxACq1dfw/DhhxEXJwcAmJsbYPXqFujUqZzE6YiyFxazRJQ1PD0A+LUD5HHKfefGQMvdnKWAcpzw8Dj0778P3t7/qtoqVcoHb+/2KFbMRsJkRNkTP8MgIuk93AX81Sa5kC3aCmi1l4Us5UhTppxUK2SHDKmC8+d7s5Al+kYsZolIWne3APs7AooE5X7JTkCLnYCeobS5iDLItGn1UKSINSwtDeHr2wF//NEUhob8oJToW/Gnh4ik889a4Fh/AEK5X7Yn0Ggtl5+lHEUIoTZnrKWlEfbs8YS5uQEKF7aWMBlRzsCeWSKSxvUlwLF+UBWyFQYBHutYyFKOcvnyK1StuhYvX4artZcv78BClkhLWMwSUea7Oh84OTx532000HAZIONbEuUMQggsXHgBtWqtx9Wrr9G58y4kJnLeWKKMwGEGRJS5XvwN/D02ef+HyUCNaVyalnKMDx9i0KvXX/Dze6Bqk8sVCA2Nha2tiYTJiHImFrNElHniwoHDXlANLfhhMlBzuqSRiLTpwoUX8PT0xYsXycMKfv65BmbObAB9fQ6hIcoILGaJKPOcHA6EP1duF6gDVJ8qbR4iLVEoBH7//TwmTvSHXK78Yy1PHmNs2tQGTZsWlzgdUc7GYpaIMsejPcC/G5XbBuZA4z95sRflCMHBUfDy2otDhx6r2mrVKojt29uhQAELCZMR5Q682oKIMl5U0H9TcP2n/hLA0lmyOETadP78C1UhK5MBkybVxsmTXixkiTIJi1kiylhCAEf7AjEhyv1ibYCyXtJmItKiVq1KYciQKrC3N8WRI90wc2YD6Onx1ytRZpEJIYTUITJTeHg4LC0tERYWBgsL/tVMlOH+WZPcK2viAHjdBkzspM1E9B3CwmJhaWmk1hYXl4iPH2ORN6+ZRKmIchZN6jX+6UhEGSf0CXBqZPJ+o7UsZClbO3nyGUqVWoaNG2+qtRsa6rGQJZIIi1kiyhiKROBgdyAhSrnv0g8o2lzaTETfSC5XYNq0U3B334ygoEgMHnwQd+8GSx2LiMDZDIgoo1z5DXhzQbltWQSot0DaPETf6M2bCHTtuhsnTwao2mrWdOICCERZBItZItK+t9eB8//NISvTAZpsBgz4ESxlP8eOPUG3bnvw7p3yEwYdHRlmzKiP8eNrQUeHq9YRZQUsZolIuxJigEPdlcMMAKDqeMCxhrSZiDSUmKjAL7+cwuzZZ5B0mbSjozm2b2+H2rULSRuOiNSwmCUi7To7EXh/V7ltX5GrfFG28+ZNBDw9fXHmTKCqrUmTYti0qQ2HFhBlQbwAjIi057k/cH2RclvXEGi6BdA1kDQSkab09HTw5MlHAICurgy//eaO/fu7sJAlyqJYzBKRdsSGAod7Ju/X/hXIU0aqNETfzM7OFNu3t0PhwlY4c6YXxo6tyfGxRFkYhxkQkXacGApEvlRuF2wAVBombR6idAoMDIOxsR7s7ExVbXXqFMKDB0Ogr68rYTIiSo/v6pmNjY3VVg4iys4e7ATubVFuG1oCHhuUsxgQZXF+fg/g6roSPXrshUKhviAmC1mi7EHj3zYKhQIzZsyAo6MjzMzM8PTpUwDA5MmTsW7dOq0HJKIsLvI1cHxg8n6DpYBFQenyEKVDfLwcI0ceRqtWO/DxYywOH36M5cuvSB2LiL6BxsXszJkzsXHjRvz2228wMEi+sKNcuXJYu3atVsMRUTZwciQQ+0G5XaIDULqrtHmIvuLZs4+oVWs9Fi26pGpr1640unUrL2EqIvpWGhezmzZtwurVq9G1a1fo6iZ/BFOhQgXcv39fq+GIKIt7ewN46KPcNrYD3FcAMl4oQ1nX7t33ULHiKly58hoAYGCgi6VLm2Dnzg6wsjKSOB0RfQuNLwB79eoVihUrlqJdoVAgISFBK6GIKJs4Pzl5+4f/AcZ5pMtC9AWxsYkYO/Yoli5NHkpQtKg1fHw6oFKlfBImI6LvpXExW6ZMGZw5cwaFCqmvgOLr64uKFStqLRgRZXGvLwBPDyi3zZ2A8gOkzUOUhoiIONStuxE3bgSp2jw9y2L16hawsDCUMBkRaYPGxeyUKVPg5eWFV69eQaFQYPfu3Xjw4AE2bdqE/fv3Z0RGIspqhFCu9JXkhymAHosCyprMzQ3h4uKAGzeCYGioiyVLmqBfv0qQcUgMUY6g8ZjZVq1aYd++fTh+/DhMTU0xZcoU3Lt3D/v27cOPP/6YERmJKKsJ9AdenFJuWxUDynpJmYboq5Yvb4pWrUri8uV+6N/fjYUsUQ4iE0KIrx+Wc4SHh8PS0hJhYWGwsLCQOg5R9iMEsO0HIOiycr/pVqB0F2kzEX3iwYMQPH8ehkaNikodhYi+kSb1msY9s0WKFMH79+9TtIeGhqJIkSKano6Ispsn+5ILWVsXoFQnafMQfWLLln/g5rYaHTvuxNOnH6WOQ0SZQONiNiAgAHK5PEV7XFwcXr16pZVQRJRFCYX6DAY1Z3ClL8oSoqMT0Lv3X+jefQ+iohIQFhaHqVNPSR2LiDJBui8A8/PzU20fOXIElpaWqn25XA5/f384OztrNRwRZTEPfIDgf5TbeasCRVtKm4cIwL//vkPHjr64ezdY1darlyv++KOJhKmIKLOku5ht3bo1AEAmk8HLS/1iD319fTg7O2P+/PlaDUdEWYgiETg/JXm/5kwukECSEkJg48abGDz4IGJiEgEApqb6WLGiGbp3ryBxOiLKLOkuZhUKBQCgcOHCuHLlCmxtbTMsFBFlQf/+CXx8pNwuUBco5C5tHsrVIiPj8dNPB7B58z+qNhcXe/j4dECpUvz9RJSbaDzP7LNnzzIiBxFlZYlxwIXpyfu1ZrFXliQjhEDTpltx5kygqm3AADcsXOgBY2N9CZMRkRS+6cqNqKgoHDx4ECtXrsSSJUvU/mlq2bJlcHZ2hpGREapVq4bLly9/8fjQ0FAMHjwY+fLlg6GhIUqUKIGDBw9+y5dBROn1z2og4r/CoXBTwLGmtHkoV5PJZBg/vhYAwNzcANu3t8PKlc1ZyBLlUhr3zN64cQNNmzZFdHQ0oqKiYGNjg5CQEJiYmMDe3h7Dhg1L97m8vb0xatQorFy5EtWqVcOiRYvg4eGBBw8ewN7ePsXx8fHx+PHHH2Fvbw9fX184Ojri+fPnsLKy0vTLIKL0SogCLs1K3q85U7osRP9p2rQ4li5tAg+PYihWzEbqOEQkIY17ZkeOHIkWLVrg48ePMDY2xsWLF/H8+XO4ubnh999/1+hcCxYsQL9+/dCrVy+UKVMGK1euhImJCdavX5/q8evXr8eHDx+wd+9e1KxZE87Ozqhbty4qVOBAf6IMc2MpEP1WuV2iPeBQUdo8lOvcuPEGY8Ycxedr/AweXJWFLBFpXszevHkTo0ePho6ODnR1dREXFwcnJyf89ttvmDhx4tdP8J/4+Hhcu3YN7u7JF5Ho6OjA3d0dFy5cSPU+fn5+qF69OgYPHgwHBweUK1cOs2fPTnXe2yRxcXEIDw9X+0dE6RQbClyZq9yW6QA1pn/xcCJtEkJg2bLL+OGHdZg//wJWrLgqdSQiyoI0Lmb19fWho6O8m729PQIDlePoLC0t8eLFi3SfJyQkBHK5HA4ODmrtDg4OCAoKSvU+T58+ha+vL+RyOQ4ePIjJkydj/vz5mDkz7Y8958yZA0tLS9U/JyendGckyvWuLQBi/1tFqXQ3IE9pafNQrhEaGosOHXZiyJBDiI9Xdlhs334HCkWuWoGdiNJB4zGzFStWxJUrV1C8eHHUrVsXU6ZMQUhICDZv3oxy5cplREYVhUIBe3t7rF69Grq6unBzc8OrV68wb948TJ06NdX7TJgwAaNGjVLth4eHs6AlSo/oYODaQuW2jh5QPfWfMSJtu3LlFTw9ffHsWaiqbcSIapg790fo6HAWDSJSp3ExO3v2bERERAAAZs2ahR49emDQoEEoXrw41q1bl+7z2NraQldXF2/fvlVrf/v2LfLmzZvqffLlywd9fX3o6uqq2kqXLo2goCDEx8fDwMAgxX0MDQ1haGiY7lxE9J/Lc4GESOW2Sz/Aqoi0eSjHE0Jg8eJL+PnnY0hIUM5tbmVlhI0bW6FVq1ISpyOirErjYrZy5cqqbXt7exw+fPibHtjAwABubm7w9/dXrS6mUCjg7++PIUOGpHqfmjVrYtu2bVAoFKqhDg8fPkS+fPlSLWSJ6BtFvgZuLVNu6xkBP/xP2jyU4334EINevf6Cn98DVdsPPxTAjh3tUKiQlXTBiCjL+6Z5ZlNz/fp1NG/eXKP7jBo1CmvWrMGff/6Je/fuYdCgQYiKikKvXr0AAD169MCECRNUxw8aNAgfPnzA8OHD8fDhQxw4cACzZ8/G4MGDtfVlEBEAXJwJJMYqtysMBszyS5uHcrxJk/zVCtmff66B06d7spAloq/SqGf2yJEjOHbsGAwMDNC3b18UKVIE9+/fx/jx47Fv3z54eHho9OCenp4IDg7GlClTEBQUBFdXVxw+fFh1UVhgYKCqBxYAnJyccOTIEYwcORLly5eHo6Mjhg8fjnHjxmn0uET0BaFPgdtrlNv6ZkDV8dLmoVxh9uyGOHz4CSIi4rBpUxs0bVpc6khElE3IxOcT96Vh3bp16NevH2xsbPDx40fkyZMHCxYswNChQ+Hp6Ynhw4ejdOmsf6VzeHg4LC0tERYWBgsLC6njEGU9h7yAu5uU2z9MBmpyOi7SPiEEZJ8tiXzrVhDy5DFBgQJ8bybK7TSp19I9zGDx4sWYO3cuQkJC4OPjg5CQECxfvhy3b9/GypUrs0UhS0RfEPESODEcuLdFuW9kDVQeLW0mypHOnHkON7fVeP06Qq29QoW8LGSJSGPpLmafPHmCDh06AADatm0LPT09zJs3DwUKFMiwcESUCUKfAEf7AWuLADeWAEJ5FTmqjAMMLaXNRjmKQiEwe/YZ1K//J27cCEKXLrsglyukjkVE2Vy6x8zGxMTAxMQEACCTyWBoaIh8+fJlWDAiymAh/wKXZgMPdiQXsACgZwxUHAZUHiNdNspx3r2LQvfue3D06BNVm0wmQ3h4HKytjSVMRkTZnUYXgK1duxZmZmYAgMTERGzcuBG2trZqxwwbNkx76YhI+4KuApdmAY/3qrcbWACugwG3EYCJvRTJKIc6efIZunTZjaAg5bzFMhkwZUpdTJ5cB7q6WptUh4hyqXRfAObs7JxisH6Kk8lkePr0qVaCZRReAEa51sszyiI24Ih6u1EeZQHrOgQwspIiGeVQcrkCM2eexvTpp1XL0ObNa4atW9uiQYPCEqcjoqxMk3ot3T2zAQEB35uLiDKbEMri9dIs4NVZ9dtM8ymHEpTvDxiYSZOPcqw3byLQrdsenDjxTNXm7l4EW7a0gYMDX29EpD0arwBGRNlEVBBwsBsQ6K/ebuEMVB0HlO2pXN2LKAOcP/9CVcjq6MgwfXo9TJhQGzo6X/6Ej4hIUyxmiXKil2eA/Z5A1JvkNptSQNUJQKnOgK6+dNkoV2jXrgwGDnSDn99DbN/eDnXqFJI6EhHlUOkeM5tTcMws5WhCAFfnA2fGA0KubDPLD9RbBJRoB8h4sQ1ljI8fY1LMShAbm4iIiDjY2ZlKlIqIsqsMWTSBiLK4uDDArx1wemxyIVuwAdD9BlCyAwtZyjCHDj1CiRJLsWXLP2rtRkZ6LGSJKMPxtxtRTvDuFrClMvB4T3JbtUlAu6OcZosyTEKCHOPGHUPTptsQEhKNgQP34/79EKljEVEu801jZp88eYINGzbgyZMnWLx4Mezt7XHo0CEULFgQZcuW1XZGIvqSOxsB/0FAYqxy38gaaLIZKNJM0liUswUGhqFz5104f/6Fqq1Bg8KwszORMBUR5UYa98z+/fffcHFxwaVLl7B7925ERionwb516xamTp2q9YBE9AWnxwFHeiUXsg5uQLfrLGQpQ/n5PYCr60pVIaunp4MFCxrhr786IU8eFrNElLk0LmbHjx+PmTNn4tixYzAwMFC1N2jQABcvXtRqOCL6gufHgSu/Je9XGAh0OgtYOksWiXK2+Hg5Ro06glatduDjR+UfUM7OVjh3rjdGjqz+1YV1iIgygsbDDG7fvo1t27alaLe3t0dICMdKEWWK+EjgaL/k/bq/A5VHS5eHcrzAwDB06LATly+/UrW1bVsa69a1hJUV5ysmIulo3DNrZWWFN2/epGi/ceMGHB0dtRKKiL7i3P+A8ADltlM9wG2klGkoFzA01EVgYBgAwMBAF3/80QS+vh1YyBKR5DQuZjt16oRx48YhKCgIMpkMCoUC586dw5gxY9CjR4+MyEhEn3p1Hri+RLmtZwT8uIbTblGGc3Aww7ZtbVGiRB6cP98bQ4ZU5bACIsoSNF40IT4+HoMHD8bGjRshl8uhp6cHuVyOLl26YOPGjdDV1c2orFrBRRMoW0uMBTZXBD7cV+7XmQdUGSNtJsqRnjz5AEtLI9jaql/QlZiogJ4e/3giooylSb32zSuABQYG4s6dO4iMjETFihVRvHjxbwqb2VjMUrZ2bjJwcaZyO28VoPN5QIerUpN2+fj8i759/VCnTiH4+XWGjg57YIkoc2lSr2n8W/Ds2bOoVasWChYsiIIFC35zSCLS0LtbwOVflds6+kCjdSxkSatiYhIwatQRrFx5DQBw4MAjrFlzDQMGVJY4GRFR2jT+rKhBgwYoXLgwJk6ciLt372ZEJiL6nCIRONJb+T8AVJsI2LlIm4lylAcPQvDDD+tUhSwAdO3qgi5d+DojoqxN42L29evXGD16NP7++2+UK1cOrq6umDdvHl6+fJkR+YgIAK7OB95dV27nKassZom0ZOvWf+Dmthr//PMWAGBsrId161pi8+Y2MDc3lDgdEdGXffOYWQB49uwZtm3bhu3bt+P+/fuoU6cOTpw4oc18Wscxs5TtfHgIbCoPyOOUsxZ0vgDkqyp1KsoBoqMTMGzYIaxbd0PVVrq0LXx8OqBcOXsJkxFRbpehY2Y/VbhwYYwfPx4VKlTA5MmT8ffff3/P6Yjoc0IBHO2rLGQBoNIIFrKkFaGhsahVaz3+/TdY1dazpyuWLm0CU1ODL9yTiChr+eb5Vc6dO4effvoJ+fLlQ5cuXVCuXDkcOHBAm9mI6NYq4NUZ5bZlEaDmDGnzUI5haWmIChXyAgBMTPTx55+tsWFDKxayRJTtaNwzO2HCBOzYsQOvX7/Gjz/+iMWLF6NVq1YwMTH5+p2JKP0UicDFT4rXRmsBff6ckXbIZDKsXNkMsbGJmDWrAUqVspU6EhHRN9G4mD19+jTGjh2Ljh07wtaWb35EGSbQH4j6b+nooq2AgvWlzUPZ2u3bb/HmTSQaNSqqajM3N8SuXR0lTEVE9P00LmbPnTuXETmI6HN3Nydvl/WSLgdla0IIrF17HcOGHYaRkR5u3BgAZ2crqWMREWlNuopZPz8/NGnSBPr6+vDz8/visS1bttRKMKJcLT4SeLRHuW1kDRRuKm0eypYiIuIwYMB+bN9+BwAQG5uIGTP+xrp1rSRORkSkPekqZlu3bo2goCDY29ujdevWaR4nk8kgl8u1lY0o93q0G0iMVm6X9AT0ONcnaebGjTfo2NEXjx9/ULX99FNlzJ/vIWEqIiLtS1cxq1AoUt0mogxyd1Pydpke0uWgbEcIgRUrrmLUqCOIi1N2LlhYGGLt2hbo0KGsxOmIiLRP46m5Nm3ahLi4uBTt8fHx2LRpUyr3ICKNRLwEAv9bfMSqGJDvB2nzULYRFhaLjh19MXjwQVUhW7lyfty4MYCFLBHlWBoXs7169UJYWFiK9oiICPTq1UsroYhytXvbAPy3MF/pboBMJmkcyh6EEPjxx83w9b2rahs+vBrOnu2FIkWsJUxGRJSxNC5mhRCQpfLL9eXLl7C0tNRKKKJcS4jPhhh0ky4LZSsymQyTJ9cBAFhZGWHPHk8sWtQYhobftdAjEVGWl+53uYoVK0Imk0Emk6Fhw4bQ00u+q1wux7Nnz9C4ceMMCUmUa7y7Cbz/V7mdvwZgVfSLhxN9qkWLkli2rCmaNi3O6beIKNdIdzGbNIvBzZs34eHhATMzM9VtBgYGcHZ2Rrt27bQekChXuffJ3LK88Iu+4OLFl/Dx+Rfz5zdS+7Tsp5+qSJiKiCjzpbuYnTp1KgDA2dkZnp6eMDIyyrBQRLmSIvG/8bIAdA2AklyZiVJSKATmzz+PiRNPIDFRgZIl82DAgMpSxyIikozGY2a9vLxYyBJlhOfHgei3yu0izZWLJRB9IiQkGi1bbsfPPx9HYqJymkRf33sQQkicjIhIOunqmbWxscHDhw9ha2sLa2vrVC8AS/Lhw4c0byOiL/j0wq/S3aXLQVnS2bOB6Nx5F16+DFe1TZhQC9On1//iezIRUU6XrmJ24cKFMDc3V23zjZNIy+IjgMd7ldtGNkARLl9LSgqFwNy5ZzF58knI5coeWDs7E2ze3AYeHsUkTkdEJL10FbNeXl6q7Z49e2ZUFqLc6+EuIDFGuV2yk3LMLOV6795FoXv3PTh69ImqrW7dQti2rR3y5zeXMBkRUdah8ZjZ69ev4/bt26r9v/76C61bt8bEiRMRHx+v1XBEuYbaLAYcYkBKEyf6qwpZmQyYMqUOjh/vwUKWiOgTGhezAwYMwMOHDwEAT58+haenJ0xMTLBz5078/PPPWg9IlOOFvwACTyq3rYsD+apJm4eyjN9++xEFC1rCwcEUx451x7Rp9aGnp/HbNhFRjqbx0jAPHz6Eq6srAGDnzp2oW7cutm3bhnPnzqFTp05YtGiRliMS5XD3toLL1xKgHB+ro5P8/bexMYafXyc4OJghb16zL9yTiCj3+qblbBUK5ZQwx48fR9OmygtVnJycEBISot10RDmdEOpDDEpz+drc6vjxp6hYcRWCgiLV2itUyMtClojoCzQuZitXroyZM2di8+bN+Pvvv9GsWTMAwLNnz+Dg4KD1gEQ52rsbwPu7ym3HWoBVEWnzUKZLTFRg8uQTaNRoM/755y26dt0NuVwhdSwiomxD42EGixYtQteuXbF3715MmjQJxYopp4bx9fVFjRo1tB6QKEe7ywu/crNXr8LRpctunD79XNVmYKCLqKgEWFgYSpiMiCj7kAktLR0TGxsLXV1d6Ovra+N0GSY8PByWlpYICwuDhYWF1HEoN1MkAqscgeh3yqm4BgZx1a9c5PDhx+jefQ9CQqIBALq6Msya1QBjx9ZUGzdLRJQbaVKvadwzm+TatWu4d+8eAKBMmTKoVKnSt56KKHcKOKosZAGgSAsWsrlEQoIckyefxNy551RtBQpYYMeOdqhZs6CEyYiIsieNi9l3797B09MTf//9N6ysrAAAoaGhqF+/Pnbs2AE7OzttZyTKmdSGGPSQLgdlmhcvwtCp0y6cP/9C1da8eQls3NgKefKYSJiMiCj70vgCsKFDhyIyMhL//vsvPnz4gA8fPuDOnTsIDw/HsGHDMiIjUc4TFw482avcNsoDFG4saRzKHOfPv1AVsnp6Opg/vxH8/DqxkCUi+g4a98wePnwYx48fR+nSpVVtZcqUwbJly9CoUSOthiPKsR7tAhJjlduluHxtbuHpWQ7+/s9w9OgTeHu3R7VqBaSORESU7WlczCoUilQv8tLX11fNP0tEX3F3U/I2ZzHIsd6/j07R67p4cWPExibC2tpYolRERDmLxsMMGjRogOHDh+P169eqtlevXmHkyJFo2LChVsMR5UjhgcCLU8pt6+JA3qpSpqEMsnv3PRQtugTbt99Wazc21mchS0SkRRr3zC5duhQtW7aEs7MznJycAAAvXrxAuXLlsGXLFq0HJMrWEqKAgGNAQkRyW+CJ5O0yPbh8bQ4TF5eIMWOOYunSKwCA/v33o3Ll/ChePI/EyYiIciaNi1knJydcv34d/v7+qqm5SpcuDXd3d62HI8q2wp4BN5YBd9YBcaFpH8fla3OUJ08+wNPTF9euvVG1NW1aHPb2phKmIiLK2TQqZr29veHn54f4+Hg0bNgQQ4cOzahcRNmPEECgP3DjD+DJPgBfWY+kcFPA0jkzklEm8PH5F337+iEiIh4AYGioi0WLGmPAADfI2PtORJRh0l3MrlixAoMHD0bx4sVhbGyM3bt348mTJ5g3b15G5iPK+uIjlXPG3lwKvL+rfpuuIVCqM5C3inq7vhlQtGXmZaQMExubiJEjD2PlymuqtuLFbeDj0wGurnklTEZElDukeznbsmXLomPHjpg6dSoAYMuWLRgwYACioqIyNKC2cTlb0prQJ8DNZcCd9UBcmPptZgUA10GASz/AhAuJ5FRPn35E27beuHXrraqtSxcXrFzZDObmhhImIyLK3jSp19JdzBobG+PevXtwdnYGoJyiy9jYGAEBAciXL993h84sLGbpuwgBPD+mHErw9ABSDCVwrA1UGgYUaw3ofPNq0ZRNBAVFokKFlXj3LgpGRnpYurQJeveuyGEFRETfSZN6Ld2/bePi4mBqmnwRg46ODgwMDBATE/PtSYmyCkUi8OYykBCZ9jEfHih7Yj8+UG/XMwJKdQEqDgXsXTM0JmUtefOaYevWthgx4jB27GiPcuXspY5ERJTraNR1NHnyZJiYJE8AHh8fj1mzZsHS0lLVtmDBAu2lI8oMoU8Av/ZA8E3N7mfuBLgOBlz6Asacdik3uHcvGA4OZrCxSZ4n1t29CG7eHAg9PY2n7SYiIi1IdzFbp04dPHig3iNVo0YNPH36VLXPj9Yo23m0FzjSM+WY1y8pUFc5lKBoSw4lyEU2bryJwYMPwt29CPbu9VR7v2MhS0QknXSPmc0pOGaWACiHFZyZCFz9ZDYO65JAyY4A0vijTM8QKNIcsCufKREpa4iMjMfgwQexadMtVdv69S3Rq1dFCVMREeVsGTJmlijHiHwDHOgEvDyd3FaiI+CxFjAwly4XZTm3b79Fx46+uH8/RNXWt29FeHqWkzAVERF9isUs5S4v/gb2ewLR/02lpKMH1P0dqDiMy8qSihAC69bdwNChhxAbmwgAMDMzwKpVzdGli4vE6YiI6FMsZil3EAK48htwdiIgFMo2M0egxU4gf3Vps1GWEhERh4EDD2DbttuqtgoVHODj0wElSvBCPyKirIbFLOV8saHAYS/giV9yW0F3oNk2LmhAat6/j0b16uvw6NEHVdtPP1XG/PkeMDLi2yURUVbEd2fK2d7eAPa1B8KSZ93AD5OB6lMBHV3pclGWZGNjjEqV8uHRow+wsDDE2rUt0KFDWaljERHRF3zTfDJnzpxBt27dUL16dbx69QoAsHnzZpw9e1ar4Yi+y+11wPbqyYWskQ3Q9iBQczoLWUqVTCbD6tUt0LFjWVy/3p+FLBFRNqBxMbtr1y54eHjA2NgYN27cQFxcHAAgLCwMs2fP1npAIo0lxACHewNH+wJy5esTeasA3a8DhZtIm42ylKtXX+Po0SdqbRYWhvD2bo+iRW0kSkVERJrQuJidOXMmVq5ciTVr1kBfX1/VXrNmTVy/fl2r4Yg09vGxsjf23w3JbRV+AjzPABaFpMtFWYoQAosXX0SNGuvQqZMvAgM1WDSDiIiyFI2L2QcPHqBOnTop2i0tLREaGqqNTETf5tFeYIsbEPzf5PZ6JkDTrYD7MuWCB0QAPnyIQZs23hgx4ggSEhT4+DEWc+dyiBQRUXalcTGbN29ePH78OEX72bNnUaRIkW8KsWzZMjg7O8PIyAjVqlXD5cuX03W/HTt2QCaToXXr1t/0uJRDCAFcnAX4tQHiw5Vt1iWBrpeB0l2kzUZZysWLL1Gx4ir89Vfy0tyjR1fHwoWNJUxFRETfQ+Nitl+/fhg+fDguXboEmUyG169fY+vWrRgzZgwGDRqkcQBvb2+MGjUKU6dOxfXr11GhQgV4eHjg3bt3X7xfQEAAxowZg9q1a2v8mJSDCAGcnQSc+19yW4mOQLcrgC0v3iElhULg99/Po3btDaohBTY2xti3rzN+/70RDAx4QSARUXYlE0IITe4ghMDs2bMxZ84cREdHAwAMDQ0xZswYzJgxQ+MA1apVQ5UqVbB06VIAgEKhgJOTE4YOHYrx48eneh+5XI46deqgd+/eOHPmDEJDQ7F37950PZ4ma/1SFicEcGokcH1xclutOUDVcVzNi1RCQqLRs+deHDjwSNVWs6YTtm9vBycnSwmTERFRWjSp1zTumZXJZJg0aRI+fPiAO3fu4OLFiwgODv6mQjY+Ph7Xrl2Du7t7ciAdHbi7u+PChQtp3m/69Omwt7dHnz59vvoYcXFxCA8PV/tHOYBQAMcHqheyDZcB1cazkCUVhUKgQYM/1QrZCRNq4eRJLxayREQ5xDcvmmBgYIAyZcp814OHhIRALpfDwcFBrd3BwQH3799P9T5nz57FunXrcPPmzXQ9xpw5czBt2rTvyklZjCIRONIbuLv5vwYZ4LEOKNdL0liU9ejoyDB9en20aeMNW1sTbNnSBh4exaSORUREWqRxMVu/fn3IvtDzdeLEie8K9CURERHo3r071qxZA1tb23TdZ8KECRg1apRqPzw8HE5OThkVkTKaPB442BV46Kvcl+kCTTYDpTtLm4uyrNatS2H58qZo1aoU8uc3lzoOERFpmcbFrKurq9p+QkICbt68iTt37sDLy0ujc9na2kJXVxdv375Va3/79i3y5s2b4vgnT54gICAALVq0ULUpFAoAgJ6eHh48eICiRYuq3cfQ0BCGhpyWKUeI+QAc7AIEHFHu6+gDzX2A4q0ljUVZx99/B+Cvvx5g/vxGan90DxpURcJURESUkTQuZhcuXJhq+y+//ILIyEiNzmVgYAA3Nzf4+/urptdSKBTw9/fHkCFDUhxfqlQp3L59W63tf//7HyIiIrB48WL2uOZkQVeBfe2B8OfKfT0joOUeoDCnVCJALldg1qwzmDbtbygUAmXL2qFPn0pSxyIiokzwzWNmP9etWzdUrVoVv//+u0b3GzVqFLy8vFC5cmVUrVoVixYtQlRUFHr1Uo5/7NGjBxwdHTFnzhwYGRmhXLlyave3srICgBTtlEMIAfyzGjg5TDnEAACMbYEWvoBTXWmzUZYQFBSJrl1348SJZ6q2vXsfoHfvil8cEkVERDmD1orZCxcuwMjISOP7eXp6Ijg4GFOmTEFQUBBcXV1x+PBh1UVhgYGB0NHReNIFygkSooDjgz650AtAvh+UQwss2AtPwPHjT9Gt2268fRsFQHnB1y+/1MXEibVZyBIR5RIazzPbtm1btX0hBN68eYOrV69i8uTJmDp1qlYDahvnmc0mPjxQDisIuZPcVmk4UOc3QNdAulyUJSQmKjBt2inMmnUGSe9g+fKZYfv2dqhb11nSbERE9P00qdc07pm1tFSfm1FHRwclS5bE9OnT0ahRI01PR5TSQ1/l1FvxEcp9fTPl1FslO0qbi7KEV6/C0aXLbpw+/VzV5uFRFJs2tYG9vamEyYiISAoaFbNyuRy9evWCi4sLrK2tMyoT5RavzgNH+wDRny5dLIDYj8m7ecoqx8fmKZXp8ShrmjDBX1XI6urKMHNmA/z8c03o6HBYARFRbqRRMaurq4tGjRrh3r17LGbp+12aBXxIfXEMAEDprsCPqwB99rZRsgULPHDixDPIZDJs394OtWoVlDoSERFJSONhBuXKlcPTp09RuHDhjMhDuUnwP8r/dfQAy09eT/rmQIVBgEsfLk1LUCiEWq+rra0JDhzoggIFLJAnj4mEyYiIKCvQuJidOXMmxowZgxkzZsDNzQ2mpuq9ZryoitIl9iMQ+VK5ne8HoNMZafNQlrR//0NMmOCP48e7w8HBTNVeoULKRVWIiCh3SvecV9OnT0dUVBSaNm2KW7duoWXLlihQoACsra1hbW0NKysrDj2g9Av5N3nb1kW6HJQlxcfLMXr0EbRosR137rxD9+57oFBoNPEKERHlEunumZ02bRoGDhyIkydPZmQeyi3efzLlli0XvKBkAQGh8PT0xeXLr1RtpqYGiIlJgKkpp2UjIiJ16S5mk6ajrVuXqy6RFgR/siwxi1n6z54999C7tx9CQ2MBAPr6Ovj990YYOrQqF0EgIqJUaTRmlr9MSGs+7ZnNw2I2t4uLS8TYscfwxx+XVW1FiljD27s9KlfOL2EyIiLK6jQqZkuUKPHVgvbDhw/fFYhyASGAkP96Zs3yA8Y20uYhST158gGenr64du2Nqq1DhzJYs6YFLC01XyKbiIhyF42K2WnTpqVYAYxIY1FvkhdG4MVfud7Fiy9VhayhoS4WLvTAwIGV+UkQERGli0bFbKdOnWBvb59RWSi3COEQA0rWtWt5+Ps/w9mzgfDx6QBXV067RURE6ZfuYpa9JKQ1Ibz4Kzd79y4K9vbq81MvXdoUcrkC5uaGEqUiIqLsKt3zzCbNZkD03T7tmbXjMIPcZNu22yhadAl8fP5Vazcx0WchS0RE3yTdxaxCoeAQA9IOVTErA2xKSxqFMkd0dAL69fND1667ERkZj759/fDkCS8WJSKi76fxcrZE30UhB97/1ytnXQzQN5E2D2W4e/eC0bGjL+7ceadqa9u2NPLmNfvCvYiIiNKHxSxlrrBnQGKMcpsXf+V4f/55Ez/9dBDR0QkAlMMJli9vCi8vV2mDERFRjsFiljLXp+NlOS1XjhUVFY+ffjqITZtuqdrKlrWDj08HlCljJ2EyIiLKaVjMUubiTAY53oMHIWjd2hv374eo2vr2rYjFi5vAxERfwmRERJQTsZgl7Xp/D7i+GIh9n/rtQVeTt1nM5kjm5oZ4/z4aAGBmZoBVq5qjSxf2whMRUcZgMUva8/oisLsxEBf29WN1DQDr4hmfiTJd/vzm2Ly5DSZM8MeOHe1RokQeqSMREVEOxmKWtOPF38Ce5kBCZPqOdx0C6PDllxPcuhWEggUtYW1trGrz8CgGd/ci0NVN9+x/RERE34TVBH2/gKPAX62TZyko2BBotAbQSWN8pK4RYGKbafEoYwghsHLlVYwceQRNmhTH7t0d1VYKZCFLRESZgcUsfZ/HfsD+DoA8XrlfpBnQwhfQM5I2F2WosLBY9Ou3Dzt33gUA7N17H1u33ka3buUlTkZERLkNi1n6dg98gINdAUWicr94O6DZNuV4WMqxrl59DU9PXzx9+lHVNnRoVXToUEbCVERElFuxmCXNxLwHri8CIl4Bd/8EhELZXror0Hgjx8HmYEII/PHHZYwZcxQJCcrvu5WVEdavb4k2bbgsMRERSYOVB2nmSG/giZ96m0tfwH0loKMrTSbKcB8/xqBPHz/s2XNf1Va1qiO8vdvD2dlKumBERJTrsZil9Ht94bNCVga4jQDq/g7IeLFPTvX2bSSqVVuL58+Tp1wbPbo6Zs9uCAMD/gFDRETSYjFL6Xd2UvJ2hYFAtUmAeQHp8lCmsLc3RZUqjnj+PAw2NsbYuLEVWrQoKXUsIiIiACxmKb2e+wMvTiq3rYoB9ZcAulyaNDeQyWRYu7YF9PV18Ouv7ihY0FLqSERERCosZunrhADOfdIrW2MaC9kc7Ny5QERHJ+DHH4uq2iwtjbBtWzsJUxEREaWOAx3p657uB95cUm7blgNKdZI2D2UIhULg11/Pom7djejceRdevgyXOhIREdFXsZilLxMK4Nz/kvdrzODFXjlQcHAUmjXbhgkT/CGXC7x/H4MFCy5IHYuIiOirOMyAvuyBDxD8j3I7bxWgWCtp85DW/f13ALp02Y3XryMAADIZMGlSbUydWk/aYEREROnAYpbSpkgEzk9N3q85S1npUI4glyswe/YZ/PLL31AoBADAwcEUW7a0hbt7EYnTERERpQ+LWUrbv5uAjw+V2wXqAoXcpc1DWhMUFIlu3XbD3/+Zqq1Bg8LYurUt8uY1kzAZERGRZljMUuoS44AL05L3a7FXNqeQyxWoX/9P3L8fAgDQ0ZFh6tS6mDSpNnR1OR6aiIiyF/7motTdXgNEBCq3CzcBHGtKm4e0RldXBzNn1gcA5MtnBn//HpgypS4LWSIiypbYM0spJUQBF2cm79ecmfaxlC21a1cGK1c2Q5s2pWFvbyp1HCIiom/GrhhK6cZSIPqtcrtEe8ChkrR56LscOfIYo0YdSdE+YEBlFrJERJTtsWeW1MWFAVfmKrdlOkCN6dLmoW+WmKjA5Mkn8Ouv5wAAFSo4wMvLVdpQREREWsaeWVJ3dQEQ+1G5XbobkKe0tHnom7x4EYZ69TaqClkAOHjwsYSJiIiIMgZ7ZilZdAhwbYFyW0cPqD71y8dTlnTgwEP06LEXHz7EAAD09HTw668NMWpUdYmTERERaR+LWUp2ZS6QEKncdukHWHHi/OwkIUGOCRP8MX9+8jK0hQpZYseO9vjhhwISJiMiIso4LGZJKfI1cHOpclvPCPjhf9LmIY0EBISiUydfXLr0StXWunUprF/fEtbWxhImIyIiylgsZknp4iwgMVa5XWEwYJZf2jykkQkT/FWFrL6+Dn7/vRGGDq0KGRe6ICKiHI7FLAFhz4Dbq5Xb+mZA1fHS5iGNLVnSGKdPP4eRkR68vdujcmX+MUJERLkDi1lSLlurSFRuu40ETGylzUNfJZcr1FbssrMzxaFDXVGokCUsLY0kTEZERJS5ODVXbvf+HnB3s3LbyBqoPFraPPRVO3f+i/LlVyI4OEqtvXx5BxayRESU67BnNqf4+AiIfqf5/Q71AIRCuV35Z8DQUru5SGtiYxMxatQRrFhxFQDQo8deHDjQBTo6HBdLRES5F4vZnOD2euBon+87h4kDUGmodvKQ1j169B4dO/ri5s0gVZu1tRHi4hJhbKwvYTIiIiJpsZjN7uIjgTNauGDrh/8B+qbffx7Suu3bb6N///2IjIwHABgZ6eGPP5qgT5+KnK2AiIhyPRaz2d2NJUBMsHI7XzUgfy3Nz2FTEnDpq91c9N1iYhIwfPhhrFlzXdVWqpQtfHzaw8XFQcJkREREWQeL2ewsNhS4Mk+5LdMBGv+pLEwp27t/PwQdOuzEnTvJ46C9vCpg2bKmMDU1kDAZERFR1sJiNju7+jsQF6rcLuPFQjYHuXTppaqQNTHRx/LlTeHl5SptKCIioiyIxWx2Ff0OuL5Iua2jD1SfImkc0i4vL1ecOBGA69ffwNu7PcqUsZM6EhERUZbEYja7ujIPSPhvntHy/QFLZ0nj0PcJCopE3rxmam3LlzeFTCaDiQlnKyAiIkoLF03IjoQAHvgot3UNgGqTpM1D30wIgXXrrqNIkcXYteuu2m2mpgYsZImIiL6CxWx2FPoEiAhUbjvWBszySZuHvklERBy6d9+Dvn33ISYmEX36+CEgIFTqWERERNkKhxlkRy9OJG8XbChdDvpmt24FoWNHXzx8+F7V1rlzuRRDDYiIiOjLWMxmR8/9k7dZzGYrQgisWnUNI0YcRlycHABgbm6AtWtbomPHshKnIyIiyn5YzGY3CVHAk73KbQMLwKGSpHEo/cLCYtG//374+PyraqtUKR98fNqjaFEbCZMRERFlXyxms5MLM4Dzn0zB5VQP0OG3MDu4c+cdWrXagadPP6rahg6tinnzfoShIb+HRERE34q/RbOTm0vV9509pMlBGrOyMkJYWKxqe/36lmjTprTEqYiIiLI/zmaQnSgSkrcrjQBc+koWhTRToIAFNm1qg2rVHHHjxgAWskRERFrCntnsyLoEUH+h1CnoC65efY3ixW1gaWmkamvatDgaNy4GHR2ZhMmIiIhyFvbMEmmREAILFlxA9err0LfvPggh1G5nIUtERKRdLGaJtOT9+2i0bLkDo0cfRWKiAr6+d7Fz592v35GIiIi+GYcZEGnB+fMv0KmTL168CFe1jRtXE23alJIwFRERUc7HYpboOygUAvPmncOkSScglyuHFNjammDz5jZo3LiYxOmIiIhyPhazRN8oODgKPXrsxeHDj1VtdeoUwrZtbeHoaCFhMiIiotyDxSzRN3j5MhzVqq3F69cRAACZDJg0qTamTq0HPT0ORSciIsos/K1L9A0cHc1RrZojAMDBwRRHj3bHjBkNWMgSERFlsizxm3fZsmVwdnaGkZERqlWrhsuXL6d57Jo1a1C7dm1YW1vD2toa7u7uXzyeKCPIZDKsW9cSPXpUwM2bA+HuXkTqSERERLmS5MWst7c3Ro0ahalTp+L69euoUKECPDw88O7du1SPP3XqFDp37oyTJ0/iwoULcHJyQqNGjfDq1atMTk65yYkTz+Dv/1StzdraGH/+2Rp585pJlIqIiIhk4vNZ3TNZtWrVUKVKFSxduhQAoFAo4OTkhKFDh2L8+PFfvb9cLoe1tTWWLl2KHj16fPX48PBwWFpaIiwsDBYW2ewinWU2QOxH5QpgvR9InSZXkMsVmD79b8yYcRq2tia4eXMg8uc3lzoWERFRjqZJvSZpz2x8fDyuXbsGd3d3VZuOjg7c3d1x4cKFdJ0jOjoaCQkJsLGxSfX2uLg4hIeHq/0jSo/XryPg7r4Z06efhhBAcHA0li7lkBYiIqKsRNJiNiQkBHK5HA4ODmrtDg4OCAoKStc5xo0bh/z586sVxJ+aM2cOLC0tVf+cnJy+OzflfEePPoGr60qcOhUAANDVlWH27AaYObOBtMGIiIhIjeRjZr/Hr7/+ih07dmDPnj0wMjJK9ZgJEyYgLCxM9e/FixeZnJKyk8REBSZO9IeHxxYEB0cDUM5ccOpUT0yYUBs6OjKJExIREdGnJJ1n1tbWFrq6unj79q1a+9u3b5E3b94v3vf333/Hr7/+iuPHj6N8+fJpHmdoaAhDQ0Ot5KWc7eXLcHTuvAtnzwaq2po2LY4//2wNW1sTCZMRERFRWiTtmTUwMICbmxv8/f1VbQqFAv7+/qhevXqa9/vtt98wY8YMHD58GJUrV86MqNKKDgEOdlNe/EUZIiFBjrp1N6oKWT09Hcyb9yP27evMQpaIiCgLk3yYwahRo7BmzRr8+eefuHfvHgYNGoSoqCj06tULANCjRw9MmDBBdfzcuXMxefJkrF+/Hs7OzggKCkJQUBAiIyOl+hIy3r3NwL2tyft6xtJlyaH09XUxZ05DAEDBgpY4c6YXxoypwWEFREREWZzky9l6enoiODgYU6ZMQVBQEFxdXXH48GHVRWGBgYHQ0UmuuVesWIH4+Hi0b99e7TxTp07FL7/8kpnRM0/sB/X9yqOlyZHDdexYFmFhsWjXrgxsbPgHAxERUXYg+TyzmS1bzjN7bjJwcaZyu/0xoFDqMzdQ+v311338/fdzLFjgIXUUIiIi+owm9ZrkPbNEmSk+Xo6ffz6GxYsvAQAqVcqHbt3SvoCQiIiIsjbJx8wSZZanTz+iZs31qkIWAI4ff/qFexAREVFWx57ZrCr8OXBhBhD1Gvj4UOo02Z6v71306eOH8PA4AICBgS4WLvTAoEG5YDYMIiKiHIzFbFZ1eS5wZ13Kdh39zM+SjcXGJmL06CNYvvyqqq1YMRv4+LRHxYr5JExGRERE2sBiNquKSmU537xVgPxpz79L6h49eg9PT1/cuJH8XHbqVA6rVjWHhQUX0iAiIsoJWMxmBz3vAqZ5AUMrQMZ5T9Nr/Hh/VSFrZKSHJUsao2/fSpDxOSQiIsoxWMxmB4ZWgJG11CmyneXLm+L8+RewtDSEj08HlC/vIHUkIiIi0jIWs5RjJCYqoKeXPEGHg4MZjhzphiJFrGFmZiBhMiIiIsooLGalEhcGXPkNiHyV+u1vr6beTqnavPkWZs8+i7NneyFPHhNVO3tjiYiIcjYWs1K5OAu4Oi99x8o4HXBaoqLiMXToIWzYcBMA4OW1F35+naGjw3GxREREuQGLWak83Z++4wq6A6bsXUzNv/++Q8eOvrh7N1jV5uBgioQEOQwN+dImIiLKDfgbXwqRr4EP95TbhlZA5wupH6ejB1gVzbRY2YUQAhs23MSQIQcRE5MIADA11cfKlc25NC0REVEuw2JWCi9OJm+7DgbylJIuSzYTGRmPgQP3Y+vW26q28uUd4O3dHqVK2UqYjIiIiKTAYlYKz/2Ttws2lC5HNnPrVhA6dvTFw4fvVW0DBrhh4UIPGBtzZTQiIqLciMVsZhMCCPyvmNUz4opeGrh69bWqkDU3N8CaNS3g6VlO4lREREQkJRazmS0iUPkPAPLXVBa0lC69e1fEiRMBuH8/BN7e7VGsmI3UkYiIiEhiLGYzW1xY8rZlEelyZAOvXoXD0dFCtS+TybB6dXPo6elwtgIiIiICAHACUynJOBdqaoQQWLr0MooWXYK9e++r3WZqasBCloiIiFRYzFKWEhoaiw4ddmLo0EOIi5OjV6+/EBgY9vU7EhERUa7ELi7KMi5ffgVPT18EBISq2nr1ckXevGbShSIiIqIsjcUsSU4IgUWLLmLcuONISFAAAKytjbBxY2u0bFlS4nRERESUlbGYJUl9+BCDXr3+gp/fA1Vb9eoFsH17OxQqZCVdMCIiIsoWWMySZG7ceINWrXbgxYtwVdvPP9fAzJkNoK+vK2EyIiIiyi5YzJJk8uQxQWRk/H/bxti0qQ2aNi0ucSoiIiLKTjibAUmmYEFL/Plna9SpUwg3bw5kIUtEREQaYzFLmeb8+RcID49Ta2vRoiROnfJCgQIWadyLiIiIKG0sZinDKRQCs2adRu3aG9C//z4IIdRul3HxCCIiIvpGLGYpQ719G4nGjbfgf/87CYVCwNv7X/z114Ov35GIiIgoHXgBGGWYEyeeoWvX3QgKigSgXL136tS6aNGihMTJiIiIKKdgMUtaJ5crMGPGaUyf/jeSRhTkzWuGbdvaon79wtKGIyIiohyFxSxp1Zs3EejadTdOngxQtf34YxFs2dIW9vam0gUjIiKiHInFLGlNQEAoqlVbi3fvogAAOjoyzJhRH+PH14KODi/yIiIiIu3jBWCkNYUKWeKHHwoAABwdzXHqlBcmTqzNQpaIiIgyDItZ0hqZTIYNG1qhT5+KuHlzIGrXLiR1JCIiIsrhOMyAvtnBg49gZKSHBg2SL+qysTHG2rUtJUxFREREuQl7ZkljCQly/PzzMTRrtg1duuxSTb1FRERElNlYzJJGAgPDULfuRsybdx4A8PZtFFavviZxKiIiIsqtOMyA0s3P7wF69tyLjx9jAQD6+jr47bcfMXx4NYmTERERUW7FYpa+Kj5ejnHjjmHRokuqNmdnK/j4tEeVKo4SJiMiIqLcjsUsfdGzZx/h6emLK1deq9rati2NdetawsrKSMJkRERERCxm6Qvi4+WoU2cjXr4MBwAYGOhiwYJG+OmnKpDJOHcsERERSY8XgFGaDAx08dtv7gCAokWtceFCHwweXJWFLBEREWUZ7JmlL+rc2QXR0Qno0KEsLCwMpY5DREREpIY9s6Ti7X0Ho0cfSdHep08lFrJERESUJbFnlhATk4ARIw5j9errAIAqVRzRqVM5iVMRERERfR17ZnO5Bw9C8MMP61SFLACcPv1cwkRERERE6cee2cwW+Sp5W89EuhwAtmz5BwMH7kdUVAIAwNhYD8uWNUXPnq6S5iIiIiJKLxazmS3wRPK2Y01JIkRHJ2Do0INYv/6mqq1MGTv4+LRH2bL2kmQiIiIi+hYsZjNboH/ydoF6mf7wd+8Go0OHnbh7N1jV1ru3K/74oylMTPQzPQ8RERHR92Axm5li3gPvbiq37VwBE9tMjzB+/HFVIWtqqo8VK5qhe/cKmZ6DiIiISBt4AVhmenEKgFBuF2woSYTVq1vA3t4ULi72uHq1PwtZIiIiytbYM5uZPh1iULBBpjxkQoIc+vq6qv28ec1w/Hh3FCtmA2NjDisgIiKi7I09s5np/b/J2461MvShhBBYvfoaXFxW4MOHGLXbXFwcWMgSERFRjsBiNjMJRfK2fsZNyxUeHocuXXZjwID9ePDgPXr1+gtCiAx7PCIiIiKpcJhBDnPjxht07OiLx48/qNqcnCyQmKhQG25ARERElBOwmM0hhBBYvvwKRo06ivh4OQDA0tIQ69a1RLt2ZSROR0RERJQxWMzmAKGhsejb1w+7dt1TtVWpkh87drRHkSLWEiYjIiIiylgsZjNDYiywrwPw6qzWT33lyit4evri2bNQVduIEdUwd+6PMDDgsAIiIiLK2VjMZoaAo8DT/cn7+maATDvX3l2//kZVyFpbG2HjxtZo2bKkVs5NRERElNWxmM0MCVHq+/UWaq2Y7d/fDSdOBCAwMAw7drRDoUJWWjkvERERUXbAYjaz1V8ElO/7zXd/8SIMTk6Wqn2ZTIb161vCwECXsxUQERFRrsN5ZrMJhUJg3rxzKFp0Cfbvf6h2m6mpAQtZIiIiypVYzGYDISHRaNFiO37++TgSEhTw8tqLV6/CpY5FREREJDkOM8hoD3cBB7t8893PnHmOzp134dWrCACATAYMHOgGBwczbSUkIiIiyrZYzGa0v8eo7+uZputuCoXAr7+exZQpJyGXK5eitbMzwZYtbdGoUVFtpyQiIiLKlljMZrT4sOTtAnWB4m2+epd376LQrdtuHDv2VNVWr54ztm1ri3z5zDMiJRER/UcIgcTERMjlcqmjEOVo+vr60NX9/mt+WMxmFusSgOeprx526dJLtG7tjaCgSADKYQVTptTF5Ml1oKvLIc5ERBkpPj4eb968QXR0tNRRiHI8mUyGAgUKwMzs+4ZOspjNYhwczBAbmwgAyJvXDFu3tkWDBoUlTkVElPMpFAo8e/YMurq6yJ8/PwwMDCCTyaSORZQjCSEQHByMly9fonjx4t/VQ8tiNotxdrbChg2tsHz5FWze3IYXehERZZL4+HgoFAo4OTnBxMRE6jhEOZ6dnR0CAgKQkJDwXcUsP7eW2KlTAYiIiFNra926FI4c6cZClohIAjo6/NVIlBm09ckHf2IlkpiowP/+dwINGvyJQYMOQAihdjs/2iIiIiL6OhazEnj1KhwNGvyJWbPOQAhg69bbOHTosdSxiIiIiLIdFrOZ7NChR3B1XYUzZwIBALq6Msyd647GjYtJnIyIiCj3efDgAfLmzYuIiAipo+QoISEhsLe3x8uXLzP8sbJEMbts2TI4OzvDyMgI1apVw+XLl794/M6dO1GqVCkYGRnBxcUFBw8ezKSk3y4hUYZx446hadNtCAlRTvni5GSB0/9v7+7jar77P4C/zqnO6ZRTLZU65CbUDJnuLOYKs6s2I7eFJFuGyc20DcOETcxdbi7DRnJZW+Fy00PUhKZiGOUuShQ2xWK6U53qvH9/WOe3o1Oc0mkn7+fj8f3jfL6fm/f3fBy9+/T9fs6J9zF7dh8IhXxbAWOMsfqZMGECBAIBBAIBDAwM0KFDB8yePRtlZWU16h48eBAeHh6QSqUwMjKCq6srIiIi1Pb7v//9D/369YOpqSlatGgBR0dHLFmyBA8fPmzkK9Kezz//HNOnT4dU2nz3cdc0z+rXr5/y39Pfj0GDBgEAKioqMGfOHHTv3h3GxsaQyWQYP3487t69q+zDwsIC48ePR0hISKNeG/APSGajo6MRHByMkJAQnD9/Hj169ICnpyfu37+vtv7JkycxZswYBAYGIjU1FUOHDsXQoUNx+fJlLUf+/G7/aYp+KwdixYqTyrLBg+2RmjoZvXvbNmFkjDHGmgsvLy/k5ubi5s2bCAsLw5YtW2okEhs2bIC3tzf69OmD06dP4+LFixg9ejSmTJmCTz9V/cbK+fPnw9fXF66urjh8+DAuX76M1atX48KFC9i5c6fWrksulzda37dv38bBgwcxYcKEBvXTmDE2lKZ5FgDs3bsXubm5yuPy5cvQ09PDqFGjAACPHz/G+fPn8cUXX+D8+fPYu3cvMjIyMGTIEJV+3n//fURGRjb+Lz/UxNzc3CgoKEj5uqqqimQyGS1btkxtfR8fHxo0aJBKWa9evWjy5MnPNV5BQQEBoIKCgvoHrYHrIe3pFckcAhYRsIgMDJbQmjUnSaFQaGV8xhhjz6e0tJTS09OptLS0qUPRWEBAAHl7e6uUDR8+nHr27Kl8ffv2bTIwMKDg4OAa7devX08A6JdffiEiotOnTxMAWrt2rdrx/vzzz1pjuXPnDo0ePZpeeeUVMjIyImdnZ2W/6uKcOXMmeXh4KF97eHhQUFAQzZw5k1q2bEn9+vWjMWPGkI+Pj0o7uVxOLVu2pB07dhDRk/whNDSU2rdvT4aGhuTo6Ei7d++uNU4iopUrV5KLi4tKWX5+Po0ePZpkMhlJJBLq1q0b/fDDDyp11MVIRHTp0iXy8vIiY2NjsrKyonHjxtEff/yhbHf48GHq06cPmZqakrm5OQ0aNIiysrLqjLGhNM2z1AkLCyOpVErFxcW11jlz5gwBoFu3bqmUd+jQgbZu3aq2TV2fOU3ytSbdZ1Yul+PcuXP4/PPPlWVCoRADBw7EqVOn1LY5deoUgoODVco8PT2xf/9+tfXLy8tRXv7/W18VFhY2PHAN2LUsgHu7Ozh0zR7t25shOnok3NxaazUGxhhjDfC9C1CSp/1xja2Bcb/Wq+nly5dx8uRJtGvXTlm2Z88eVFRU1FiBBYDJkydj3rx5+PHHH9GrVy9ERkaiRYsWmDp1qtr+zczM1JYXFxfDw8MDrVu3RkxMDKytrXH+/HkoFAqN4t+xYwc++ugjpKSkAACysrIwatQoFBcXK78tKj4+Ho8fP8awYU++Jn7ZsmX4/vvvsXnzZnTu3BknTpzAuHHjYGlpCQ8PD7XjJCUlwcXFRaWsrKwMzs7OmDNnDkxMTBAbGwt/f3907NgRbm5utcb46NEjDBgwABMnTkRYWBhKS0sxZ84c+Pj44NixYwCAkpISBAcHw9HREcXFxVi4cCGGDRuGtLS0WreECw0NRWhoaJ3vV3p6Otq2bVujvD55ljrbtm3D6NGjYWxsXGudgoICCASCGv823NzckJSUhMDAwOceT1NNmszm5+ejqqoKrVq1Uilv1aoVrl27prZNXl6e2vp5eer/o1m2bBkWL178YgKuB6EQ2DFmPxYcG4bl+7fCzMywyWJhjDFWDyV5QPHvTR3FMx08eBAtWrRAZWUlysvLIRQK8Z///Ed5PjMzE6amprCxsanRViQSwc7ODpmZmQCA69evw87ODgYGBhrF8MMPP+CPP/7A2bNnYW5uDgDo1EnzB5w7d+6MFStWKF937NgRxsbG2LdvH/z9/ZVjDRkyBFKpFOXl5QgNDUVCQgLc3d0BAHZ2dkhOTsaWLVtqTWZv3bpVI5lt3bq1SsI/ffp0xMfHY9euXSrJ7NMxfvXVV+jZs6dK4hkeHg5bW1tkZmbC3t4eI0aMUBkrPDwclpaWSE9PR7du3dTGOGXKFPj4+NT5fslkMrXl9cmznnbmzBlcvnwZ27Ztq7VOWVkZ5syZgzFjxsDExKRGbKmpqc81Vn01+28A+/zzz1VWcgsLC2Frq8X7VMdfggUImz/RB4w5kWWMMZ1jbK0T4/bv3x+bNm1CSUkJwsLCoK+vXyN5el701N7nzystLQ09e/ZUJrL15ezsrPJaX18fPj4+iIyMhL+/P0pKSnDgwAFERUUBeLJy+/jxY7z99tsq7eRyOXr27FnrOKWlpTA0VP3ZXFVVhdDQUOzatQu///475HI5ysvLa3wr3NMxXrhwAcePH1euHP/djRs3YG9vj+vXr2PhwoU4ffo08vPzlSvWt2/frjWZNTc3b/D72RDbtm1D9+7dVRL5v6uoqICPjw+ICJs2bapxXiKR4PHjx40aY5MmsxYWFtDT08O9e/dUyu/duwdra/UfYmtra43qi8ViiMXiFxNwfUj5lgLGGNNp9fxTv7YZGxsrV0HDw8PRo0cPbNu2TfnnXXt7exQUFODu3bs1VvLkcjlu3LiB/v37K+smJyejoqJCo9VZiURS53mhUFgjUa6oqFB7LU/z8/ODh4cH7t+/jyNHjkAikcDLywvAk9sbACA2NhatW6v+3K0rB7CwsMCff/6pUrZy5UqsW7cOa9euVT6t//HHH9d4yOvpGIuLizF48GB8/fXXNcapXg0fPHgw2rVrh++++w4ymQwKhQLdunWr8wGyhtxmUJ886+9KSkoQFRWFJUuWqD1fncjeunULx44dq7EqCwAPHz6EpaXlM8dqiCbdzUAkEsHZ2RlHjx5VlikUChw9elT5Z4Knubu7q9QHgCNHjtRanzHGGHvZCIVCzJs3DwsWLEBpaSkAYMSIETAwMMDq1atr1N+8eTNKSkowZswYAMDYsWNRXFyMb775Rm3/jx49Ulvu6OiItLS0Wp9et7S0RG5urkpZWlrac11T7969YWtri+joaERGRmLUqFHKRPu1116DWCzG7du30alTJ5Wjrr/G9uzZE+np6SplKSkp8Pb2xrhx49CjRw+V2y/q4uTkhCtXrqB9+/Y1YjA2NsaDBw+QkZGBBQsW4K233kKXLl1qJNLqTJkyBWlpaXUetd1mUJ886+92796N8vJyjBs3rsa56kT2+vXrSEhIQMuWLdX2cfny5TpXx1+IZz4i1siioqJILBZTREQEpaen06RJk8jMzIzy8vKIiMjf35/mzp2rrJ+SkkL6+vq0atUqunr1KoWEhJCBgQFdunTpucbT9m4GjDHGdENz282goqKCWrduTStXrlSWhYWFkVAopHnz5tHVq1cpKyuLVq9eTWKxmD755BOV9rNnzyY9PT367LPP6OTJk5STk0MJCQk0cuTIWnc5KC8vJ3t7e+rbty8lJyfTjRs3aM+ePXTy5EkiIoqLiyOBQEA7duygzMxMWrhwIZmYmNTYzWDmzJlq+58/fz699tprpK+vT0lJSTXOtWzZkiIiIigrK4vOnTtH69evp4iIiFrft5iYGLKysqLKykpl2axZs8jW1pZSUlIoPT2dJk6cSCYmJirvr7oYf//9d7K0tKSRI0fSmTNnKCsri+Li4mjChAlUWVlJVVVV1LJlSxo3bhxdv36djh49Sq6urgSA9u3bV2uMDfWsPIuoZq5V7c033yRfX98a5XK5nIYMGUJt2rShtLQ0ys3NVR7l5eXKeiUlJSSRSOjEiRNqY3tRuxk0eTJLRLRhwwZq27YtiUQicnNzU27hQfTkH0xAQIBK/V27dpG9vT2JRCLq2rUrxcbGPvdYnMwyxhhTp7kls0REy5YtI0tLS5UtlQ4cOEB9+/YlY2NjMjQ0JGdnZwoPD1fbb3R0NP3rX/8iqVRKxsbG5OjoSEuWLKlza66cnBwaMWIEmZiYkJGREbm4uNDp06eV5xcuXEitWrUiU1NTmjVrFk2bNu25k9n09HQCQO3atauxxaVCoaC1a9eSg4MDGRgYkKWlJXl6etLPP/9ca6wVFRUkk8koLi5OWfbgwQPy9vamFi1akJWVFS1YsIDGjx//zGSWiCgzM5OGDRtGZmZmJJFI6NVXX6WPP/5YGeuRI0eoS5cuJBaLydHRkRITExs9mSWqO8+qvp6nc61r164RAPrpp59q9JednU0A1B7Hjx9X1vvhhx/IwcGh1rheVDIrIKrnXd46qrCwEKampigoKFB7bwdjjLGXU1lZGbKzs9GhQ4caDwWx5mvjxo2IiYlBfHx8U4fS7LzxxhuYMWMGxo4dq/Z8XZ85TfK1Zr+bAWOMMcZYbSZPnoxHjx6hqKioWX+lrbbl5+dj+PDhyvuwGxMns4wxxhh7aenr62P+/PlNHUazY2FhgdmzZ2tlrCbdzYAxxhhjjLGG4GSWMcYYY4zpLE5mGWOMsb95yZ6LZqzJvKjPGiezjDHGGKDcgL+xv3qTMfZE9Tef6enpNagffgCMMcYYw5MfqGZmZrh//z4AwMjICAKBoImjYqx5UigU+OOPP2BkZAR9/Yalo5zMMsYYY3+p/r766oSWMdZ4hEIh2rZt2+BfGjmZZYwxxv4iEAhgY2MDKysrVFRUNHU4jDVrIpEIQmHD73jlZJYxxhh7ip6eXoPv42OMaQc/AMYYY4wxxnQWJ7OMMcYYY0xncTLLGGOMMcZ01kt3z2z1Br2FhYVNHAljjDHGGFOnOk97ni9WeOmS2aKiIgCAra1tE0fCGGOMMcbqUlRUBFNT0zrrCOgl+94+hUKBu3fvQiqVamUz7MLCQtja2uLOnTswMTFp9PHYi8dzqPt4DnUfz6Fu4/nTfdqeQyJCUVERZDLZM7fveulWZoVCIdq0aaP1cU1MTPgDrON4DnUfz6Hu4znUbTx/uk+bc/isFdlq/AAYY4wxxhjTWZzMMsYYY4wxncXJbCMTi8UICQmBWCxu6lBYPfEc6j6eQ93Hc6jbeP503z95Dl+6B8AYY4wxxljzwSuzjDHGGGNMZ3EyyxhjjDHGdBYns4wxxhhjTGdxMssYY4wxxnQWJ7MvwMaNG9G+fXsYGhqiV69eOHPmTJ31d+/ejVdffRWGhobo3r07Dh06pKVIWW00mcPvvvsOffv2xSuvvIJXXnkFAwcOfOacs8an6eewWlRUFAQCAYYOHdq4AbJn0nQOHz16hKCgINjY2EAsFsPe3p7/P21Cms7f2rVr4eDgAIlEAltbW8yaNQtlZWVaipY97cSJExg8eDBkMhkEAgH279//zDaJiYlwcnKCWCxGp06dEBER0ehxqkWsQaKiokgkElF4eDhduXKFPvzwQzIzM6N79+6prZ+SkkJ6enq0YsUKSk9PpwULFpCBgQFdunRJy5GzaprO4dixY2njxo2UmppKV69epQkTJpCpqSn99ttvWo6cVdN0DqtlZ2dT69atqW/fvuTt7a2dYJlams5heXk5ubi40LvvvkvJycmUnZ1NiYmJlJaWpuXIGZHm8xcZGUlisZgiIyMpOzub4uPjycbGhmbNmqXlyFm1Q4cO0fz582nv3r0EgPbt21dn/Zs3b5KRkREFBwdTeno6bdiwgfT09CguLk47Af8NJ7MN5ObmRkFBQcrXVVVVJJPJaNmyZWrr+/j40KBBg1TKevXqRZMnT27UOFntNJ3Dp1VWVpJUKqUdO3Y0VojsGeozh5WVldS7d2/aunUrBQQEcDLbxDSdw02bNpGdnR3J5XJthcjqoOn8BQUF0YABA1TKgoODqU+fPo0aJ3s+z5PMzp49m7p27apS5uvrS56eno0YmXp8m0EDyOVynDt3DgMHDlSWCYVCDBw4EKdOnVLb5tSpUyr1AcDT07PW+qxx1WcOn/b48WNUVFTA3Ny8scJkdajvHC5ZsgRWVlYIDAzURpisDvWZw5iYGLi7uyMoKAitWrVCt27dEBoaiqqqKm2Fzf5Sn/nr3bs3zp07p7wV4ebNmzh06BDeffddrcTMGu6flM/oa33EZiQ/Px9VVVVo1aqVSnmrVq1w7do1tW3y8vLU1s/Ly2u0OFnt6jOHT5szZw5kMlmNDzXTjvrMYXJyMrZt24a0tDQtRMiepT5zePPmTRw7dgx+fn44dOgQsrKyMHXqVFRUVCAkJEQbYbO/1Gf+xo4di/z8fLz55psgIlRWVmLKlCmYN2+eNkJmL0Bt+UxhYSFKS0shkUi0FguvzDLWAMuXL0dUVBT27dsHQ0PDpg6HPYeioiL4+/vju+++g4WFRVOHw+pJoVDAysoK3377LZydneHr64v58+dj8+bNTR0aew6JiYkIDQ3FN998g/Pnz2Pv3r2IjY3Fl19+2dShMR3EK7MNYGFhAT09Pdy7d0+l/N69e7C2tlbbxtraWqP6rHHVZw6rrVq1CsuXL0dCQgIcHR0bM0xWB03n8MaNG8jJycHgwYOVZQqFAgCgr6+PjIwMdOzYsXGDZirq8zm0sbGBgYEB9PT0lGVdunRBXl4e5HI5RCJRo8bM/l995u+LL76Av78/Jk6cCADo3r07SkpKMGnSJMyfPx9CIa+1/dPVls+YmJhodVUW4JXZBhGJRHB2dsbRo0eVZQqFAkePHoW7u7vaNu7u7ir1AeDIkSO11meNqz5zCAArVqzAl19+ibi4OLi4uGgjVFYLTefw1VdfxaVLl5CWlqY8hgwZgv79+yMtLQ22trbaDJ+hfp/DPn36ICsrS/mLCABkZmbCxsaGE1ktq8/8PX78uEbCWv2LCRE1XrDshflH5TNaf+SsmYmKiiKxWEwRERGUnp5OkyZNIjMzM8rLyyMiIn9/f5o7d66yfkpKCunr69OqVavo6tWrFBISwltzNTFN53D58uUkEoloz549lJubqzyKioqa6hJeeprO4dN4N4Omp+kc3r59m6RSKU2bNo0yMjLo4MGDZGVlRV999VVTXcJLTdP5CwkJIalUSj/++CPdvHmTfvrpJ+rYsSP5+Pg01SW89IqKiig1NZVSU1MJAK1Zs4ZSU1Pp1q1bREQ0d+5c8vf3V9av3prrs88+o6tXr9LGjRt5ay5dtmHDBmrbti2JRCJyc3OjX375RXnOw8ODAgICVOrv2rWL7O3tSSQSUdeuXSk2NlbLEbOnaTKH7dq1IwA1jpCQEO0HzpQ0/Rz+HSez/wyazuHJkyepV69eJBaLyc7OjpYuXUqVlZVajppV02T+KioqaNGiRdSxY0cyNDQkW1tbmjp1Kv3555/aD5wREdHx48fV/myrnreAgADy8PCo0eb1118nkUhEdnZ2tH37dq3HTUQkIOL1fMYYY4wxppv4nlnGGGOMMaazOJlljDHGGGM6i5NZxhhjjDGmsziZZYwxxhhjOouTWcYYY4wxprM4mWWMMcYYYzqLk1nGGGOMMaazOJlljDHGGGM6i5NZxhgDEBERATMzs6YOo94EAgH2799fZ50JEyZg6NChWomHMca0hZNZxlizMWHCBAgEghpHVlZWU4eGiIgIZTxCoRBt2rTB+++/j/v377+Q/nNzc/HOO+8AAHJyciAQCJCWlqZSZ926dYiIiHgh49Vm0aJFyuvU09ODra0tJk2ahIcPH2rUDyfejLHnpd/UATDG2Ivk5eWF7du3q5RZWlo2UTSqTExMkJGRAYVCgQsXLuD999/H3bt3ER8f3+C+ra2tn1nH1NS0weM8j65duyIhIQFVVVW4evUqPvjgAxQUFCA6Olor4zPGXi68MssYa1bEYjGsra1VDj09PaxZswbdu3eHsbExbG1tMXXqVBQXF9faz4ULF9C/f39IpVKYmJjA2dkZv/76q/J8cnIy+vbtC4lEAltbW8yYMQMlJSV1xiYQCGBtbQ2ZTIZ33nkHM2bMQEJCAkpLS6FQKLBkyRK0adMGYrEYr7/+OuLi4pRt5XI5pk2bBhsbGxgaGqJdu3ZYtmyZSt/Vtxl06NABANCzZ08IBAL069cPgOpq57fffguZTAaFQqESo7e3Nz744APl6wMHDsDJyQmGhoaws7PD4sWLUVlZWed16uvrw9raGq1bt8bAgQMxatQoHDlyRHm+qqoKgYGB6NChAyQSCRwcHLBu3Trl+UWLFmHHjh04cOCAcpU3MTERAHDnzh34+PjAzMwM5ubm8Pb2Rk5OTp3xMMaaN05mGWMvBaFQiPXr1+PKlSvYsWMHjh07htmzZ9da38/PD23atMHZs2dx7tw5zJ07FwYGBgCAGzduwMvLCyNGjMDFixcRHR2N5ORkTJs2TaOYJBIJFAoFKisrsW7dOqxevRqrVq3CxYsX4enpiSFDhuD69esAgPXr1yMmJga7du1CRkYGIiMj0b59e7X9njlzBgCQkJCA3Nxc7N27t0adUaNG4cGDBzh+/Liy7OHDh4iLi4Ofnx8AICkpCePHj8fMmTORnp6OLVu2ICIiAkuXLn3ua8zJyUF8fDxEIpGyTKFQoE2bNti9ezfS09OxcOFCzJs3D7t27QIAfPrpp/Dx8YGXlxdyc3ORm5uL3r17o6KiAp6enpBKpUhKSkJKSgpatGgBLy8vyOXy546JMdbMEGOMNRMBAQGkp6dHxsbGymPkyJFq6+7evZtatmypfL19+3YyNTVVvpZKpRQREaG2bWBgIE2aNEmlLCkpiYRCIZWWlqpt83T/mZmZZG9vTy4uLkREJJPJaOnSpSptXF1daerUqURENH36dBowYAApFAq1/QOgffv2ERFRdnY2AaDU1FSVOgEBAeTt7a187e3tTR988IHy9ZYtW0gmk1FVVRUREb311lsUGhqq0sfOnTvJxsZGbQxERCEhISQUCsnY2JgMDQ0JAAGgNWvW1NqGiCgoKIhGjBhRa6zVYzs4OKi8B+Xl5SSRSCg+Pr7O/hljzRffM8sYa1b69++PTZs2KV8bGxsDeLJKuWzZMly7dg2FhYWorKxEWVkZHj9+DCMjoxr9BAcHY+LEidi5c6fyT+UdO3YE8OQWhIsXLyIyMlJZn4igUCiQnZ2NLl26qI2toKAALVq0gEKhQFlZGd58801s3boVhYWFuHv3Lvr06aNSv0+fPrhw4QKAJ7cIvP3223BwcICXlxfee+89/Pvf/27Qe+Xn54cPP/wQ33zzDcRiMSIjIzF69GgIhULldaakpKisxFZVVdX5vgGAg4MDYmJiUFZWhu+//x5paWmYPn26Sp2NGzciPDwct2/fRmlpKeRyOV5//fU6471w4QKysrIglUpVysvKynDjxo16vAOMseaAk1nGWLNibGyMTp06qZTl5OTgvffew0cffYSlS5fC3NwcycnJCAwMhFwuV5uULVq0CGPHjkVsbCwOHz6MkJAQREVFYdiwYSguLsbkyZMxY8aMGu3atm1ba2xSqRTnz5+HUCiEjY0NJBIJAKCwsPCZ1+Xk5ITs7GwcPnwYCQkJ8PHxwcCBA7Fnz55ntq3N4MGDQUSIjY2Fq6srkpKSEBYWpjxfXFyMxYsXY/jw4TXaGhoa1tqvSCRSzsHy5csxaNAgLF68GF9++SUAICoqCp9++ilWr14Nd3d3SKVSrFy5EqdPn64z3uLiYjg7O6v8ElHtn/KQH2NM+ziZZYw1e+fOnYNCocDq1auVq47V92fWxd7eHvb29pg1axbGjBmD7du3Y9iwYXByckJ6enqNpPlZhEKh2jYmJiaQyWRISUmBh4eHsjwlJQVubm4q9Xx9feHr64uRI0fCy8sLDx8+hLm5uUp/1fenVlVV1RmPoaEhhg8fjsjISGRlZcHBwQFOTk7K805OTsjIyND4Op+2YMECDBgwAB999JHyOnv37o2pU6cq6zy9sioSiWrE7+TkhOjoaFhZWcHExKRBMTHGmg9+AIwx1ux16tQJFRUV2LBhA27evImdO3di8+bNtdYvLS3FtGnTkJiYiFu3biElJQVnz55V3j4wZ84cnDx5EtOmTUNaWhquX7+OAwcOaPwA2N999tln+PrrrxEdHY2MjAzMnTsXaWlpmDlzJgBgzZo1+PHHH3Ht2jVkZmZi9+7dsLa2VvtFD1ZWVpBIJIiLi8O9e/dQUFBQ67h+fn6IjY1FeHi48sGvagsXLsR///tfLF68GFeuXMHVq1cRFRWFBQsWaHRt7u7ucHR0RGhoKACgc+fO+PXXXxEfH4/MzEx88cUXOHv2rEqb9u3b4+LFi8jIyEB+fj4qKirg5+cHCwsLeHt7IykpCdnZ2UhMTMSMGTPw22+/aRQTY6z54GSWMdbs9ejRA2vWrMHXX3+Nbt26ITIyUmVbq6fp6enhwYMHGD9+POzt7eHj44N33nkHixcvBgA4Ojri559/RmZmJvr27YuePXti4cKFkMlk9Y5xxowZCA4OxieffILu3bsjLi4OMTEx6Ny5M4AntyisWLECLi4ucHV1RU5ODg4dOqRcaf47fX19rF+/Hlu2bIFMJoO3t3et4w4YMADm5ubIyMjA2LFjVc55enri4MGD+Omnn+Dq6oo33ngDYWFhaNeuncbXN2vWLGzduhV37tzB5MmTMXz4cPj6+qJXr1548OCByiotAHz44YdwcHCAi4sLLC0tkZKSAiMjI5w4cQJt27bF8OHD0aVLFwQGBqKsrIxXahl7iQmIiJo6CMYYY4wxxuqDV2YZY4wxxpjO4mSWMcYYY4zpLE5mGWOMMcaYzuJkljHGGGOM6SxOZhljjDHGmM7iZJYxxhhjjOksTmYZY4wxxpjO4mSWMcYYY4zpLE5mGWOMMcaYzuJkljHGGGOM6SxOZhljjDHGmM76P2UPaDGel1EKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制ROU曲线\n",
    "fpr_rf, tpr_rf, _ = roc_curve(y_test, rf_clf.predict_proba(x_test)[:, 1])\n",
    "roc_auc_rf = auc(fpr_rf, tpr_rf)\n",
    "# 可视化\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr_rf, tpr_rf, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_rf)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve for Random Forest')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型优化\n",
    "# 方法一\n",
    "# 设置参数网格\n",
    "param_grid = {\n",
    "    'n_estimators': [10, 50, 100],     # 决策树的数量,数值越大，模型可能越好，但是也能会导致过拟合\n",
    "    'max_depth': [None, 10, 20, 30],    # 决策树的最大深度，控制模型的复杂度\n",
    "    'min_samples_split': [2, 5, 10],   # 决定节点分裂的最小样本数量\n",
    "    'min_samples_leaf': [1, 2, 4],     # 决定叶节点的最小样本数量\n",
    "    'max_features': ['auto', 'sqrt', 'log2']  # 考虑分裂时的特征数量\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "540 fits failed out of a total of 1620.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "195 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\python\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\python\\lib\\site-packages\\sklearn\\base.py\", line 1145, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"c:\\python\\lib\\site-packages\\sklearn\\base.py\", line 638, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"c:\\python\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 96, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "345 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\python\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\python\\lib\\site-packages\\sklearn\\base.py\", line 1145, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"c:\\python\\lib\\site-packages\\sklearn\\base.py\", line 638, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"c:\\python\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 96, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan 0.96209342 0.97352292 0.97051268\n",
      " 0.95185426 0.96210427 0.96630305 0.91215131 0.9440356  0.94524585\n",
      " 0.93621332 0.94765006 0.94825247 0.93863562 0.95186693 0.95487174\n",
      " 0.91636637 0.93203264 0.9338272  0.8995206  0.91456637 0.92178444\n",
      " 0.90073628 0.9260013  0.92419769 0.89894171 0.91758566 0.92419407\n",
      " 0.96871269 0.96992113 0.97352292 0.94704946 0.96148739 0.96390065\n",
      " 0.92116936 0.94585911 0.94825971 0.93140128 0.95367235 0.95246572\n",
      " 0.92719889 0.94826151 0.94765006 0.91938746 0.93441514 0.93261695\n",
      " 0.9019212  0.92419407 0.91757299 0.90135316 0.91397301 0.91878505\n",
      " 0.89413148 0.92119288 0.92119107        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.95005789 0.95788017 0.96148739 0.93200007 0.95367235 0.95547596\n",
      " 0.91336336 0.93864467 0.94043381 0.91698144 0.9470567  0.9482579\n",
      " 0.92540251 0.9386302  0.94405188 0.91275914 0.92900973 0.93081515\n",
      " 0.90071819 0.90976338 0.92118926 0.88207062 0.92360795 0.91757661\n",
      " 0.88509353 0.9061453  0.9169742  0.93258801 0.95787474 0.96630667\n",
      " 0.93201274 0.95548138 0.95366873 0.91335794 0.92961033 0.93743623\n",
      " 0.91456456 0.94284887 0.94645248 0.90434169 0.93021817 0.94283983\n",
      " 0.89773689 0.92540251 0.92961033 0.88087666 0.91157061 0.91276638\n",
      " 0.88869532 0.91458085 0.90975976 0.88027063 0.90613807 0.91035674\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan 0.96510366 0.97532653 0.97472955\n",
      " 0.95187597 0.96389703 0.96149463 0.92659105 0.94465429 0.94464887\n",
      " 0.9350284  0.95307536 0.95246753 0.92840551 0.94825971 0.95547234\n",
      " 0.91997178 0.92961214 0.93623503 0.90252723 0.92059771 0.93141214\n",
      " 0.89471761 0.92178986 0.92599949 0.91095734 0.91578386 0.9284037\n",
      " 0.96149463 0.96991932 0.9717175  0.94706393 0.96991932 0.96510185\n",
      " 0.91937299 0.94706574 0.94405369 0.93983321 0.94945548 0.95667535\n",
      " 0.92419046 0.95005789 0.94946272 0.91336698 0.93743261 0.93262238\n",
      " 0.89410796 0.92238504 0.92238865 0.90014291 0.91458265 0.91877781\n",
      " 0.89532002 0.91396396 0.91337603        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.96149282 0.97352473 0.97713376 0.94524585 0.96870907 0.96389522\n",
      " 0.92358624 0.94104164 0.94825428 0.93142118 0.95788379 0.95607656\n",
      " 0.92900431 0.95246753 0.95306451 0.91575129 0.93382177 0.93743985\n",
      " 0.90552661 0.92238865 0.92480191 0.90795434 0.91998806 0.91998082\n",
      " 0.89289953 0.92178986 0.92900792 0.96089222 0.97533377 0.97111871\n",
      " 0.94585188 0.96510004 0.96450487 0.91697239 0.94825428 0.9470585\n",
      " 0.93081153 0.95668078 0.94945729 0.94283802 0.94345309 0.95186874\n",
      " 0.91216216 0.93201274 0.9332266  0.89954412 0.9169941  0.92179529\n",
      " 0.89351098 0.91818083 0.91336517 0.89050074 0.909767   0.91519049]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters found: {'max_depth': 30, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "# 创建一个随机森林分类器对象\n",
    "rf = RandomForestClassifier()\n",
    "# verbose=0 表示不输出日志，verbose=1 表示输出总体的进度信息，verbose=2输出更详细的进度信息\n",
    "grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=5, n_jobs=-1, verbose=1)\n",
    "grid_search.fit(new_x_train, new_y_train)\n",
    "print('Best parameters found:', grid_search.best_params_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.95      0.90       356\n",
      "           1       0.41      0.17      0.24        69\n",
      "\n",
      "    accuracy                           0.83       425\n",
      "   macro avg       0.63      0.56      0.57       425\n",
      "weighted avg       0.78      0.83      0.79       425\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 评估优化后模型\n",
    "best_rf = grid_search.best_estimator_\n",
    "y_pred_orf = best_rf.predict(x_test)\n",
    "class_report_orf = classification_report(y_test, y_pred_orf)\n",
    "print(class_report_orf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制混淆矩阵\n",
    "cm_orf = confusion_matrix(y_test, y_pred_orf)\n",
    "# 可视化\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm_orf, annot=True, fmt='g', cmap='Blues',\n",
    "            xticklabels=['Predicted 0', 'Predicted 1'],\n",
    "            yticklabels=['Actual 0', 'Actual 1'])\n",
    "plt.title('Confusion Matrix for Optimized Random Forest Model')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制ROU曲线\n",
    "fpr_orf, tpr_orf, _ = roc_curve(y_test, best_rf.predict_proba(x_test)[:, 1])\n",
    "roc_auc_orf = auc(fpr_orf, tpr_orf)\n",
    "# 可视化\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr_orf, tpr_orf, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_orf)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve for Optimized Random Forest')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The top 10 selected features:\n",
      "Age\n",
      "Education\n",
      "JobInvolvement\n",
      "JobLevel\n",
      "MaritalStatus\n",
      "MonthlyIncome\n",
      "YearsAtCompany\n",
      "YearsInCurrentRole\n",
      "YearsWithCurrManager\n",
      "Department_Research & Development\n"
     ]
    }
   ],
   "source": [
    "# 方法二\n",
    "# 特征选择\n",
    "# 选择最强相关性的10个特征\n",
    "selector = SelectKBest(score_func=mutual_info_classif, k=10)\n",
    "x_train_selected = selector.fit_transform(x_train, y_train)\n",
    "x_test_selected = selector.transform(x_test)\n",
    "\n",
    "# 获取选择后的特征索引\n",
    "selected_feature_indices = selector.get_support(indices=True)\n",
    "\n",
    "# 获取原始特征数据中与选择后的特征对应的列\n",
    "selected_features = x_train.columns[selected_feature_indices]\n",
    "\n",
    "# 输出最强相关的10个特征\n",
    "print(\"The top 10 selected features:\")\n",
    "for feature in selected_features:\n",
    "    print(feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用选择后的特征训练模型\n",
    "best_rf = RandomForestClassifier()\n",
    "best_rf.fit(x_train_selected, y_train)\n",
    "\n",
    "# 在测试集上进行预测\n",
    "y_pred_orf = best_rf.predict(x_test_selected)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.97      0.91       356\n",
      "           1       0.59      0.19      0.29        69\n",
      "\n",
      "    accuracy                           0.85       425\n",
      "   macro avg       0.73      0.58      0.60       425\n",
      "weighted avg       0.82      0.85      0.81       425\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 输出分类报告\n",
    "class_report = classification_report(y_test, y_pred_orf)\n",
    "print(class_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制混淆矩阵\n",
    "cm_orf = confusion_matrix(y_test, y_pred_orf)\n",
    "# 可视化\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm_orf, annot=True, fmt='g', cmap='Blues',\n",
    "            xticklabels=['Predicted 0', 'Predicted 1'],\n",
    "            yticklabels=['Actual 0', 'Actual 1'])\n",
    "plt.title('Confusion Matrix for Optimized Random Forest Model')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.99      0.91       356\n",
      "           1       0.71      0.07      0.13        69\n",
      "\n",
      "    accuracy                           0.84       425\n",
      "   macro avg       0.78      0.53      0.52       425\n",
      "weighted avg       0.83      0.84      0.79       425\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 方法三\n",
    "# 建立逻辑回归模型\n",
    "logistic_regression = LogisticRegression(max_iter=1000)  # 增加最大迭代次数\n",
    "logistic_regression.fit(x_train, y_train)\n",
    "\n",
    "# 在测试集上进行预测\n",
    "y_pred_orf = logistic_regression.predict(x_test)\n",
    "\n",
    "# 输出分类报告\n",
    "class_report = classification_report(y_test, y_pred_orf)\n",
    "print(class_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制混淆矩阵\n",
    "cm_orf = confusion_matrix(y_test, y_pred_orf)\n",
    "# 可视化\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm_orf, annot=True, fmt='g', cmap='Blues',\n",
    "            xticklabels=['Predicted 0', 'Predicted 1'],\n",
    "            yticklabels=['Actual 0', 'Actual 1'])\n",
    "plt.title('Confusion Matrix for Logistic Regression Model')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制ROU曲线\n",
    "fpr_orf, tpr_orf, _ = roc_curve(y_test, logistic_regression.predict_proba(x_test)[:, 1])\n",
    "roc_auc_orf = auc(fpr_orf, tpr_orf)\n",
    "# 可视化\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr_orf, tpr_orf, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_orf)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve for Logistic Regression')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 重要特征\n",
    "rf_feature_importance = best_rf.feature_importances_\n",
    "feature_names = new_x_train.columns\n",
    "rf_feature_df = pd.DataFrame({\n",
    "    'Feature': feature_names,\n",
    "    'Importance': rf_feature_importance\n",
    "})\n",
    "# 筛选出重要特征\n",
    "sorted_rf_feature_df = rf_feature_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "print(sorted_rf_feature_df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
